Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARISON OF TABLE JOIN EXECUTION TIME FOR PARALLEL DBMS AND MAPREDUCE

Analysis of existing research work indicates that preference for implementation of queries to structured data is given to parallel DBMS. MapReduce (MR) is perceived as supplementary to DBMS technology. We attempt to figure out behavior pattern of parallel rowstorage DBMS and MR system Hadoop on the example of Join task depending on the variation of the parameters that in other authors’ experiments do not vary or differ from ours. This article presents detailed process models for table joins in the parallel row-storage DBMS and MRsystem, as well as the results of detailed calculation experiments performed on these models. The models were set up for various scalability schemes for MR (number of nodes) and DMBS (data volume in a node) and fragmentation of the joined tables by the primary key. The following parameters were varied: queried data selectivity, number of sorted resulting records and cardinality of the grouping attribute. The modeling results showed that with the increase of the stored data volume parallel DBMS starts losing against MR-system at certain thresholds.
Prediction of Indian summer monsoon rainfall (ISMR) using canonical correlation analysis of global circulation model products

The Canonical Correlation Analysis (CCA) method has been used in this study for improving General Circulation Model (GCM) predicted rainfall over India during the southwest monsoon season. Hindcast runs for 27 years (1982–2008) from six GCM outputs are used. This statistical technique relates the pattern of multivariate predictor field (model rainfall) to the pattern of predictand fields (observed rainfall). It is found that the CCA method improves the skill of three of the GCMs at the all‐India level. A noticeable improvement is also observed in the composite prediction with CCA as compared to the simple mean of raw GCM products. The skill of the composite prediction after applying CCA is higher compared to the simple mean of raw model products in several homogeneous zones such as the hilly areas, west central area and over some parts of northwest India. The possible reason for the improvement in the skill of some of the GCMs may be the similarity between the loading patterns of model predictions and the observed rainfall. Copyright © 2012 Royal Meteorological Society
Peer-to-peer supercomputing: de-centalized data tracking and processing

This project focuses on several workflow optimizations, as well as a message delivery system guaranteeing transactional message delivery with persistence and in-order server-side handling for Copernicus, an application for parallel adaptive molecular dynamics developed by the department of Theoretical Physics at the Royal Institute of Technology in Stockholm in 2011. We introduce and briefly explain the Copernicus workflow and show how the optimizations increase performance. We also conclude that the message delivery system performs with a negligible overhead in worst-case scenarios.
Combined optimal power allocation and adaptive modulation for MIMO systems with imperfect CSI

This paper presents combination of optimal power adaptation and adaptive modulation for MIMO systems with imperfect channel state information (CSI). Proposed algorithm is designed to enable high-reliable transmission with as much possible capacity, convenient for high data rate FEC systems. Effect of use of Reed-Solomon code in the case of imperfect CSI is also considered.
Time Synchronism of Dynamic Password Status Authentication

The e-commerce transaction process customers are most worried about is the security issues. Targeted e-commerce transaction authentication security that prevail in question time synchronization based on a dynamic password authentication technology, and gives a concrete realization of identity authentication process. The main advantage of this technology is a dynamic one-time passwords, and simple operation, transmission using RSA encryption algorithm. Network Time Protocol NTP for time synchronization of standard internet protocols, to solve the difficult problem of time synchronization.
Survey and Analysis of Medium Access Control Protocols for Wireless Sensor Network

Wireless sensor networks are appealing to researchers due to their wide range of application in several fields such as military settings, critical infrastructure protection, target detection and tracking, environmental monitoring, industrial process monitoring etc. Communication among wireless sensor nodes is usually achieved by means of a unique channel. It is the characteristic of this channel that only a single node can transmit a message at any given time. Therefore, shared access of the channel requires the establishment of a MAC protocol among the sensor nodes. The objective of the MAC protocol is to regulate access to the shared wireless medium. For sensor nodes that are battery powered, it is sometimes difficulty or impractical to charge or replace exhausted battery. So the medium access protocol (MAC) for wireless sensor network must be energy efficient. In this paper, we outline the sensor network properties that are crucial for the design of MAC layer protocols. Then, describe four MAC protocols proposed for sensor networks emphasizing their strengths and weaknesses and compared those protocols. Analysis of power consumption and latency performance for these four MAC protocol is also shown in this report.
Multiple access interference suppression with linear chip equalizers in WCDMA downlink receivers

The 3rd generation cellular communications standard will be based on wideband CDMA (WCDMA). To obtain adequate performance multiuser receivers, such as LMMSE receivers, can be used. Unfortunately, previously suggested LMMSE receivers cannot be applied in WCDMA systems employing long scrambling codes. In this paper, linear receivers suitable also for downlink with long scrambling codes are studied. These receivers equalize the channel prior to the despreading, thus restoring the orthogonality of users and suppressing multiple access interference (MAI). The numerical results show that chip equalizer based receivers can offer significant performance improvement in comparison to the conventional RAKE receiver.
Gerald Beresford Whitham. 13 December 1927 — 26 January 2014

Gerald Beresford Whitham was one of the leading applied mathematicians of the twentieth century. His original, deep and insightful research into nonlinear wave propagation formed the foundation of and mathematical techniques for much of the current research in this area. Indeed, many of these ideas and techniques have spread beyond wave propagation research into other areas, such as reaction–diffusion, and has influenced research in pure mathematics. His textbook Linear and nonlinear waves, published in 1974, is still the standard reference for the mathematics of wave motion. Whitham was also instrumental in building from scratch the Department of Applied Mathematics at the California Institute of Technology and, through choosing key people in new, promising research areas, in making it into one of the leading centres of applied mathematics in the world, with an influence far beyond its small size. During his academic career, Whitham received major awards and prizes for his research. He was elected a Fellow of the Royal Society in 1965 and a Fellow of the American Academy of Arts and Sciences in 1959, and was awarded the Norbert Wiener Prize for Applied Mathematics in 1980.
Learning by Fusing Heterogeneous Data

It has become increasingly common in science and technology to gather data about systems at different levels of granularity or from different perspectives. This often gives rise to data that are represented in totally different input spaces. A basic premise behind the study of learning from heterogeneous data is that in many such cases, there exists some correspondence among certain input dimensions of different input spaces. In our work we found that a key bottleneck that prevents us from better understanding and truly fusing heterogeneous data at large scales is identifying the kind of knowledge that can be transferred between related data views, entities and tasks. We develop interesting and accurate data fusion methods for predictive modeling, which reduce or entirely eliminate some of the basic feature engineering steps that were needed in the past when inferring prediction models from disparate data. In addition, our work has a wide range of applications of which we focus on those from molecular and systems biology: it can help us predict gene functions, forecast pharmacological actions of small chemicals, prioritize genes for further studies, mine disease associations, detect drug toxicity and regress cancer patient survival data. Another important aspect of our research is the study of latent factor models. We aim to design latent models with factorized parameters that simultaneously tackle multiple types of data heterogeneity, where data diversity spans across heterogeneous input spaces, multiple types of features, and a variety of related prediction tasks. Our algorithms are capable of retaining the relational structure of a data system during model inference, which turns out to be vital for good performance of data fusion in certain applications. Our recent work included the study of network inference from many potentially nonidentical data distributions and its application to cancer genomic data. We also model the epistasis, an important concept from genetics, and propose algorithms to efficiently find the ordering of genes in cellular pathways. A central topic of our Thesis is also the analysis of large data compendia as predictions about certain phenomena, such as associations between diseases and involvement of genes in a certain phenotype, are only possible when dealing with lots of data. Among others, we analyze 30 heterogeneous data sets to assess drug toxicity and over 40 human gene association data collections, the largest number of data sets considered by a collective latent factor model up to date. We also make interesting observations about deciding which data should be considered for fusion and develop a generic approach that can estimate the sensitivities between different data sets.
Keyword based Movie Recommendation Service using MapReduce

Recommender Systems provide customers with information in the form of recommendations that helps to decide which products to purchase. In recent days these traditional Recommender Systems are facing a service overload problems as the data is increasing tremendously and analyzing such large dataset is a challenging task. Such large volume of data is termed as Big Data. Most of the existing service Recommender Systems provides same rating to users without considering diverse user’s preferences. User based Collaborative Filtering approach for recommending appropriate service to users based on their preferences has been proposed. The model uses weighted average approach to calculate the personalized rating of a service for the active user. The proposed model will mainly focus on custom-made service recommendations which use keywords to indicate current user-preferences and the model is implemented in HADOOP to meet its scalability and efficiency. Keywords—Recommender Systems, Big Data, Collaborative Filtering, HADOOP.
Computational efficiency of the Gabor transform

The two-dimensional Gabor transform has been proposed as a model of the visual receptive field of the simple cells of the mammalian striate cortex. The authors analyze the computational steps needed to achieve this transformation and present typical execution times for several computers. The use of an adaptive iteration step size is shown to improve performance significantly. Computational requirements are examined for both sequential and parallel computers. A parallel computer with global addition capability and with a processor for each pixel could perform a Gabor transform in real time. On existing computers, real-time performance is not possible for moderate sized images. Sequential computers show better cost/performance ratios than parallel machines. <<ETX>>
Organizing Computational Problem Solving Communities via Collusion-Resistant Semantic Game Tournaments

Competitions have been successfully used to build and organize communities aiming to solve complex computational problems. Recently, the winner of an open online competition to solve a complex immunogenomics problem has produced an algorithm that has a higher accuracy and is 1,000 times faster than the corresponding algorithm developed by the US National Institutes of Health. State of the art computational problem solving competitions follow the contest pattern where a trusted administrator is responsible for preparing an objective, anonymous, neutral, correct and thorough process to evaluate participants. Depending on the computational problem of interest, it can be a massive undertaking for the administrator to prepare such evaluation process. For example, consider the effort involved in collecting 2.86 Gigabytes worth of compressed benchmarks for the SAT 2013 competition. The thesis of this dissertation is “semantic games of interpreted predicate logic sentences provide a useful foundation for organizing computational problem solving communities”. Semantic games of interpreted predicate logic sentences specifying computational problems provide a correct, systematic approach for participants to assist the administrator in evaluating their opponents and therefore require a significantly lower administrator overhead. In this dissertation, we tackle a key challenge of fending against collusion potential which can render the results of semantic game tournaments meaningless. We report on the surprising discovery that it is in fact possible to fend against collusion potential in semantic game tournaments by using certain ranking functions. We give a, first of its kind, formal characterization of collusion-resistant ranking functions for semantic game tournaments. We also give a representation theorem of ranking functions possessing the limited collusion effect property as well as other basic monotonicity properties. In essence, we show that under basic monotonicity
Comparison Between Ipv4 And Ipv6 Using Opnet Simulator

The overall objective of the paper is to evaluate and compare the performance of IP (IPv4 and IPv6), using OPNET (1) as a simulation tool to compare them in terms of different criteria. In this, we have designed a wide area networks based on IPv4and the IPv6 protocols in OPNET, a network simulator platform. Simulation techniques allow Lena analyze the behavior of each network separately. The network consists of different components such as servers, routers, and adapters. The drive of this paper is to assess the basic productivity, and packet loss, a delay, jitter, and the use of methods based on simulative and analytical network addresses in IPv4 / IPv6. The aim of this paper is to find out what is the best IP under a particular application. Understanding the impact of the delay, and the wrath of use and productivity in the network addresses of IPv4/IPv6. The simulation has been carried out using Optimized Network Engineering Tool (OPNET).
The Role Of Perception And Action In Intelligent Systems

Robotics plays an important role in the applications of Artificial Intelligence in Engineering, since it deals with the interaction of an intelligent system with the world by means of perception and action. However, Robotics has been traditionaly considered as just a mere application area of AI. In this paper, the role of perception and action in current AI systems is analyzed and some recent fundamental results concerning the use of Robotic Intelligence for applications of AI in Engineering are discussed, in order to build artificial systems that behave in an intelligent way in the real world. Finally, two implemented systems based on this methodology are described to show the relevance of the proposed strategy to Engineering applications, one using visual perception, and another based on force/torque sensing.
Formal Security Validation of Cloud Security Protocols using Avispa Tool

Cloud computing has attracted a lot of attention in the recent years by providing different kind of services to the users through the internet. Basically in cloud, users can store cloud objects and access those objects from the cloud storage. To provide the access privilege to the user as well as storing cloud objects in encrypted form is a challenging task Fine grained access control in cloud is also required as cloud servers are usually provided by commercial providers who are not in the same trusted domain with the users, as well as sometimes cloud object owners are also from different trusted domain, as per security requirement cloud object owners privacy also has to be maintained. To solve all these type of security problems various solutions are proposed in literature, but little attention has been paid to diagnosis all those proposed schemes In this current project we have studied all the security related protocols specifically fine grained access control protocols used for cloud infrastructure. We then compare all those proposed schemes as per security wise. We have used AVISPA (Automated validation of Internet security Protocols and Applications) tool for building and analyzing formal security models of the proposed schemes for formal security validation. General Terms--Cloud computing, Access control, Avispa, Validation.
ENERGY EFFICIENT SENSOR SELECTION IN VISUAL SENSOR NETWORKS BASED ON MULTI-OBJECTIVE OPTIMIZATION

In this paper, we investigate the problem of visual coverage in visual sensor networks (VSNs). It is required to select a subset of sensor nodes to provide a visual coverage over the monitoring region at each point of time. In contrast with the pervious works which considered only single metric for sensor selection method, in this study we assumed the sensor selection as multi-criteria problem. For the purpose of maximizing the network lifetime, we consider three metrics a) visual coverage ratio, i.e., percentage of monitoring region which is fully covered by camera sensors, b) number of selected sensors, i.e., number of active sensors for covering the desired region, and c) overlapping coverage ratio, i.e., percentage of monitoring region which is covered by more than one camera sensor. Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the problem. Besides, impact of steady state selection and generational selection method is studied on the network lifetime. Simulation results show the superiority of multi-objective optimization. NSGA-II results not only longer network lifetime but also fewer number of active sensor and lower overlapping ratio at each point of time.
Carbamazepine furfural hemisolvate

In the title compound, C15H12N2O·0.5C5H4O2, carbamazepine mol­ecules retain the R22(8) N—H⋯O hydrogen-bonded dimer arrangement observed in the crystal structures of each of the four known an­hydro­us polymorphs. The furfural mol­ecule is located between adjacent carbamazepine dimers and is hydrogen bonded to only one of the anti-oriented NH groups available on the dimer.
SISTEM DETEKSI KECEPATAN KENDARAAN BERMOTOR PADA REAL TIME TRAFFIC INFORMATION SYSTEM

Pada tugas akhir ini akan dibuat suatu sistem dan program untuk mendeteksi kecepatan kendaraan bermotor dari sebuah rekaman video lalu-lintas. Hasil deteksi kecepatan akan dijadikan acuan penegakkan aturan lalu-lintas. Program yang dibuat menggunakan metode background reconstruction dan frame difference. Metode background reconstruction menghasilkan background dari sejumlah frame video dan dijadikan referensi untuk ekstraksi foreground. Metode frame difference merupakan ekstraksi foreground dari selisih sebuah frame dengan referensi background. Obyek bergerak akan diukur perpindahannya untuk menempuh suatu jarak tertentu sebagai referensi perhitungan kecepatan. Penggunaan metode frame difference dan background reconstruction untuk mendeteksi pergerakan dan perhitungan waktu dan jarak tempuh bisa dilakukan pada rekaman video lalu-lintas. Ketepatan pengukuran mencapai 98% pada kecepatan acuan 50km/j. Akurasi terendah terjadi pada kecepatan kendaraan 80km/j dengan ketepatan hanya 93%. Tingkat akurasi tergantung pada besarnya pergeseran per frame, adanya bayangan dan terjadinya penumpukan obyek kendaraan. Dengan adanya tugas akhir ini diharapkan bisa melengkapi penelitian modul Speed Enforcement System (SES) pada penelitian Intelligent Transportation System.
Automatic feature learning and parameter estimation for Hidden Markov models using MCE and Gibbs sampling

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy AUTOMATIC FEATURE LEARNING AND PARAMETER ESTIMATION FOR HIDDEN MARKOV MODELS USING MCE AND GIBBS SAMPLING By Xuping Zhang December 2009 Chair: Dr. Paul Gader Major: Computer Engineering Hidden Markov models (HMMs) are useful tools for landmine detection using Ground Penetrating Radar (GPR), as well as many other applications. The performance of HMMs and other feature-based methods depends not only on the design of the classifier but also on the features. Few studies have investigated both classifiers and feature sets as a whole system. Features that accurately and succinctly represent discriminating information in an image or signal are very important to any classifier. In addition, when the system that generated those original images has to change to fit different environments, the features usually have to be modified. The process of modification can be laborious and may require a great deal of application domain specific knowledge. Hence, it is worthwhile to investigate methods of automatic feature learning for purposes of automating algorithm development. Even if the discrimination capability is unchanged, there is still value to feature learning in terms of time saved. In this dissertation, two new methods are explored to simultaneously learn parameters intended to extract features and learn parameters for image-based classifiers. The notion of an image is general here. For example, a sequence of time or frequency domain features. We have developed a generalized, parameterized model of feature extraction based on morphological operations. More specifically, the model includes hit-and-miss masks to extract the shape of interests in the images. In one method, we use the minimum classification error (MCE) method with generalized
A holistic customer experience design on the internet

This paper proposes a holistic customer experience framework on the internet. According to the framework, we can effectively manage the direct experience, indirect experience, primary experience and secondary experience through web interface, and enhancing the holistic customer experience to pleasurable experience(including vivid experience, consummatory experience and aesthetic experience ), which can unite the consumer’s sensory, emotional, social, and intellectual experiences in a new and positive way. The framework proposed here can achieve a more successful customer pleasurable experiences and increase customer satisfaction on the internet. Furthermore, we make the theories and applications of customer experience will be more extensive and convenient for researches and businesses. Keyword: Holistic Customer Experience, Web Interface, Pleasurable Experience