Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Business Intelligence to cloud environments

Business Intelligence systems use information technology to supply integrated management support with data coming from several sources of structured and unstructured data. The integrated infrastructures of Business Intelligence (BI) are often too complex and hence costly and inflexible. A solution for these issues is to leverage cloud computing services to enhance legacy BI systems and applications with cost-efficient increased scalability and flexibility. However, the migration of BI systems to cloud environments is usually hindered by strict requirements regarding privacy, security, or availability and a multitude of interdependences with other systems. In this paper, we describe the challenges in the adoption of BI within cloud environments and propose a cloud migration framework to assist decision makers in taking into account the consequences of the migration of BI systems to cloud environments as well as the impact of privacy, security, cost, and performance in so doing.
Speech Recognition using the Epochwise Back Propagation through Time Algorithm

s paper, the artificial neural networks are implemented to accomplish the English alphabet speech recognition. The design an accurate and effective speech recognition system is a challenging task in the area of speech recognition. We implemented a new data classification method, where we use neural networks, which are trained and performance can be defined on the basis of recognition rate. This method gave comparable result to the already implemented neural networks. In this paper, Back propagation neural network architecture used to recognize the time varying input data, and provides better accurate results for the English Alphabet speech recognition. The Epochwise Back Propagation through time (BPTT) algorithm uses the epoch values of input signal to train the network structures and yields the satisfactory results.
Using spatial relations of objects in real world scenes for scene structuring and scene understanding

Given a room full of individual objects in a generic household scene, one can observe that the objects are mostly not placed randomly but in a certain order. Because of this each object can be described by the surrounding objects and the spatial relations to those objects. This paper presents several types of spatial relationships that can be deduced using object positions in 3D as well as an approach to retrieve these relations from real world scenes via annotation of colored 3D pointclouds gathered with a sensor. Finally, a way to use this data to make predictions about an unknown object based on its surrounding objects is presented.
Fingerprint Liveness Detection Using Curvelet Energy and Co-Occurrence Signatures

This paper proposes a new curvelet transform-based method to detect spoof fingerprint attacks in fingerprint biometric systems. It uses only one image to differentiate a real fingerprint from a spoof one. It is based on the observation that, real and spoof fingerprints exhibit different textural characteristics. Textural measures based on curvelet energy signatures and curvelet co-occurrence signatures are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running pudil's sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost. M1, support vector machine and k-nearest neighbor; then we fuse all the mentioned classifiers using the "majority voting rule" to form an Ensemble classifier. Classification rates achieved with these classifiers for energy signatures range from ~94.12% to ~97.41%. Likewise, classification rates for co-occurrence signatures range from ~94.35% to ~98.12%. Thus, the performance of a new liveness detection approach is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.
Increased natural killer cell cytotoxicity and NKp30 expression protects against hepatitis C virus infection in high‐risk individuals and inhibits replication in vitro

CD56pos natural killer (NK)/natural T (NT) cells are important innate effectors providing the first line of defense against viral infection. Enhanced NK activity has been shown to protect from human immunodeficiency virus‐1 infection. However, the role played by these innate effectors in protection against or development of hepatitis C virus (HCV) infection is unknown. We characterized CD56pos populations in 11 injection drug users (IDUs) who remained uninfected despite being repeatedly exposed to HCV. NK profiles in exposed but uninfected (EU) individuals were compared with preinfection samples (median 90 days prior to HCV seroconversion) collected from 14 IDUs who were exposed and subsequently became infected (EI) and unexposed normal control subjects (n = 8). Flow cytometric analysis of CD56pos populations demonstrated that EUs had a higher proportion of CD56low mature (P = 0.0011) NK cells compared with EI subjects. Bead‐isolated NKs (>90% purity) from EUs had significantly higher interleukin‐2 (IL‐2)–induced cytolytic activity against the NK‐sensitive cell line K562 at an effector‐to‐target ratio of 10:1 (P < 0.0001). NKp30, a natural cytotoxicity receptor involved in NK activation, is highest on NK/NT cells in EUs relative to infected subjects. Using the JFH‐1 infection system, we demonstrated that NKp30high cells in the absence of exogenous stimulation significantly reduce infection of hepatocytes. Conclusion: CD56pos populations in EUs are enriched for effector NKs displaying enhanced IL‐2–induced cytolytic activity and higher levels of the natural cytotoxicity receptor NKp30‐activating receptor. In addition, NKp30high cells are more effective in preventing infection of Huh‐7.5 cells than their NKp30low/neg counterparts. These data support the hypothesis that NK cells contribute to anti‐HCV defense in vivo in the earliest stages of infection, providing innate protection from HCV acquisition. (HEPATOLOGY 2010)
Real-time parallel spatial video denoising schemes on multi-core processors

Noises in videos can occur during recording and transmission. The advancement of the processor technology makes real time video denoising possible on multicore processors. In this paper, we investigate parallel techniques for denoising the real-time video on a multi-core processor. We compare two strategies: a block strategy, which assigns a group of threads to each block of video frames and a distributor strategy, which uses one thread to distribute the frame data to each thread. From our experiments with the total variation based image denoising technique, we found that by using the distributor strategy, we can achieve speedup which is 1.28 times faster than the block strategy and the video frame rate can be increased by 18.44%.
Eqr470: Simulation of Lévy Processes *

Simulation methods of Lévy processes based on random walk approximation, series representations of Lévy processes, and on Poisson and Gaussian approximations are reviewed. Formulae for simulation of some Lévy processes are given as examples. 1 Lévy processes Lévy processes arise in many areas of applied probability. See, e.g., relevant articles eqr456, eqr461, eqr473, eqr478, eqr450, and eqr472. For theory of Lévy processes the reader is referred to books of Bertoin [3] and Sato [14] which are the classical references on this topic. A stochastic process {X(t)} t≥0 is said to be a Lévy process if it has stationary and independent increments and satisfies X(0) = 0. One may always assume that sample paths of a Lévy process are right continuous and have left limits. Standard Brownian motion {B(t)} is a Lévy process, so is a linear Brownian motion {µt + σB(t)}. Poisson process {N (t)} with rate λ is a Lévy process, so is a compound Poisson process. Linear combination of independent Lévy processes is again a Lévy process. In particular, the sum of a linear Brownian motion and a compound Poisson process X(t) = µt + σB(t) + N (t) j=1 V j (1) is a Lévy process, where V j are i.i. d. and {B(t)}, {N λ (t)}, and {V j } are independent of each other. The key to understanding a Lévy process {X(t)} is the structure of its jumps J(t) = X(t) − X(t−). It occurs that for any Borel subset B of R \ {0}, the counting process of B, N B (t) = Card{s ≤ t : J(s) ∈ B} is a Poisson process with rate ν(B), where ν is a measure satisfying ν({0}) = 0 and min{x 2 , 1} ν(dx) < ∞.
Software Defined Mobile Multicast

Mobile multicast has been deployed in telecommunication networks for information dissemination applications such as IPTV and video conferencing. Recent studies of mobile multicast focused on fast handover protocols, and algorithms for multicast tree management have witnessed little improvement over the years. Shortest path trees represent the status quo of multicast topology in real-world systems. Steiner trees were investigated extensively in the theory community and are known to be bandwidth efficient, but come with an associated complexity. Recent developments in the Software Defined Networking (SDN) paradigm have shed light on implementing more sophisticated protocols for better routing performance. We propose an SDN-based design to combat the complexity vs. Performance dilemma in mobile multicast. We construct low-cost Steiner trees for multicastin a mobile network, employing an SDN controller for coordinating tree construction and morphing. Highlights of our design include a set of efficient online algorithms for tree adjustment when nodes arrive and depart on the fly, and an SDN rule update framework based on constraints expressed by boolean logic to ensure loop free rule updates. The algorithms are proven to achieve a constant competitive ratio against the offline optimal Steiner tree, with an amortized constant number of edge swaps per adjustment. Mininet-based implementation and evaluation further validate the efficacy of our design.
Constrained Relay Node Placement in Energy Harvesting Wireless Sensor Networks

The constrained relay node placement problem in a wireless sensor network is concerned with deploying a minimum number of relay nodes (RNs) in a set of candidate locations in the network to satisfy a specific requirement(s), such as connectivity or survivability. In this paper, we study the constrained relay node placement problem in an energy harvesting network. In such a network, it is imperative that the placement be energy harvesting aware, because the more energy the placed nodes can harvest the more effective the network can be. In our study, the RNs are constrained to be placed at only the candidate locations, where the energy harvesting potential of the locations are known a priori. Our aim is to place a minimum number of relay nodes, to achieve connectivity or survivability, while ensuring that the relay nodes harvest large amounts of ambient energy. For both the connectivity and survivability, we study the problems, prove that they are NP-hard, and propose polynomial time O(1)approximation algorithms with low approximation ratios. We also validate the effectiveness and efficiency of our algorithms through simulations and show that the RNs placed by our algorithms harvest 50% more energy on average, in comparison to those placed by the algorithms unaware of energy harvesting.
Online Learning for Group Lasso

We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional batch-mode group lasso algorithms, which suffer from the inefficiency and poor scalability, our proposed algorithm performs in an online mode and scales well: at each iteration one can update the weight vector according to a closed-form solution based on the average of previous subgradients. Therefore, the proposed online algorithm can be very efficient and scalable. This is guaranteed by its low worst-case time complexity and memory cost both in the order of O(d), where d is the number of dimensions. Moreover, in order to achieve more sparsity in both the group level and the individual feature level, we successively extend our online system to efficiently solve a number of variants of sparse group lasso models. We also show that the online system is applicable to other group lasso models, such as the group lasso with overlap and graph lasso. Finally, we demonstrate the merits of our algorithm by experimenting with both synthetic and real-world datasets.
Is There a Free-Rider Problem in Lobbying? Endogenous Tariffs, Trigger Strategies, and the Number of Firms

There are two reasons we expect moreconcentrated industries to have advantages over less-concentrated industries in playing the lobbying game. ' One is that in a noncooperative setting, as the number firms rises, we expect the free-rider problem to become worse (this is the finding of Rodrik, 1986). The second is that we expect that the ability to maintain a cooperative outcome deteriorates as the number of firms in the industry rises. [The classic reference is Mancur Olson (1965); also, see Russell Hardin (1982) and Todd Sandler (1992) among others. ] In contrast with the second intuition, this paper shows that there is no general presumption that maintaining cooperation becomes more difficult as the number of firms in the industry rises. The ability of an industry to overcome the free-rider problem is analyzed in a re-peated tariff lobbying game where cooperation may be maintained through the use of a simple trigger strategy.2 The difficulty of maintaining cooperation is measured by the critical value of the discount parameter. All values above this critical value are consistent with cooperation, and all values below the critical value imply that cheating will prevent maintenance of the cooperative outcome. There is no presumption that this critical value rises with the number of firms. A separate, but related, question involves asymptotic results. Standard intuition suggests that as industry structure approaches perfect competition, it becomes impossible to maintain cooperation. In the context of this paper, this intuition corresponds to the critical value of the discount factor converging to I as the number of firms increases to infinity. In the general case it is shown that the critical value of the discount parameter converges to a value less than 1 in the limit as the number of firms becomes arbitrarily large. If the actual discount parameter lies above this value, then cooperation may be maintained even with an infinite number of firms. Though the framework here is quite simple, it provides results which contrast sharply to our standard intuition about how industry structure affects the ability to maintain cooperation. ' There are two ways to interpret the results of this paper. One is to assert that standard intuition is wrong, and that there is, in fact, no presumption that maintaining cooperation becomes more difficult as the number
ON STATISTICAL ANALYSIS OF SYNCHRONOUS STREAM CIPHERS

Synchronous stream ciphers produce long keystreams to be XORed with plaintext. The output keystreams should be indistinguishable from truly random sequences and should not leak any information about the secret key and the internal state of the cipher. In this study, we propose six new statistical tests to evaluate the randomness properties of synchronous stream ciphers. We applied four of these tests to the ciphers presented to ECRYPT and tabulated the results.
Fast probabilistic fault diagnosis for large scale system

The challenges of fast fault diagnosis for large scale service systems are analyzed in this paper. A multi-layer management model is proposed to model the service scenario, which builds bipartite Bayesian network to denote dependence relationships. An incremental fault belief assessment method is proposed to analyze symptoms and compute posterior fault probabilities in an event-driven manner. Based on the method, we propose a greedy fault diagnosis algorithm to produce a sub-optimal explanation. To reduce the complexity of fault selection, we transform the fault diagnosis problem of finding MPE into finding most likely assignment of each fault, and propose corresponding algorithm. Simulation results prove the validity and efficiency of our algorithms.
P62dok, a Negative Regulator of Ras and Mitogen-Activated Protein Kinase (Mapk) Activity, Opposes Leukemogenesis by P210bcr-abl

p62dok has been identified as a substrate of many oncogenic tyrosine kinases such as the chronic myelogenous leukemia (CML) chimeric p210bcr-abl oncoprotein. It is also phosphorylated upon activation of many receptors and cytoplamic tyrosine kinases. However, the biological functions of p62dok in normal cell signaling as well as in p210bcr-abl leukemogenesis are as yet not fully understood. Here we show, in hemopoietic and nonhemopoietic cells derived from p62dok−/− mice, that the loss of p62dok results in increased cell proliferation upon growth factor treatment. Moreover, Ras and mitogen-activated protein kinase (MAPK) activation is markedly sustained in p62dok−/− cells after the removal of growth factor. However, p62dok inactivation does not affect DNA damage and growth factor deprivation–induced apoptosis. Furthermore, p62dok inactivation causes a significant shortening in the latency of the fatal myeloproliferative disease induced by retroviral-mediated transduction of p210bcr-abl in bone marrow cells. These data indicate that p62dok acts as a negative regulator of growth factor–induced cell proliferation, at least in part through downregulating Ras/MAPK signaling pathway, and that p62dok can oppose leukemogenesis by p210bcr-abl.
Consumptive Nature-based Recreation in the United States: Welfare Measures from Truncated and Untruncated Count Data Models

In the context of a recent controversy over the trend in nature-based recreation demand, this paper estimates the demand for and welfare value of consumptive nature-based recreation (CNR) in the United States and examines if this value changed between 1996 and 2006. Demand is estimated using the travel cost model framework and the standard, truncated and zero-inflated count data models. The hypothesis that the demand for CNR trips was identical in the two sample years is rejected. Although total participation declined between 1996 and 2006, results suggest that aggregate welfare value of CNR, in 2006 constant dollars, was higher in 2006. An important unresolved issue regarding inconsistencies in welfare measures from truncated and untruncated count data models is also addressed in this paper. Larger welfare estimates are obtained from the untruncated models. This result is robust across different combinations of truncated and untruncated models and the two sample years.
Digital Repository @ Iowa State University

Oblivious RAM (ORAM) is a provable technique to protect a user’s access pattern to outsourced data. Recently, many ORAM constructions have been proposed, but most of them are impractical due to high communication and user-side storage costs. Motivated by Partition ORAM (P-ORAM) [16], a state-of-the-art communication-efﬁcient ORAM construction, this paper proposes GP-ORAM (Generalized Partition ORAM) as a new framework to assemble multiple ORAM partitions together while overcoming the limitations of the P-ORAM construction. GP-ORAM allows smaller and adjustable number of partitions, fully utilizes the available user-side storage to reduce communication cost, and can ef-ﬁciently export the index table to the server. As a result, GP-ORAM incurs low bandwidth cost (i.e., O (log N ) data blocks per query in practice) and has signiﬁ-cantly less user-side storage cost than P-ORAM. We demonstrate the security and practicality of GP-ORAM through extensive performance analysis.
Empirical Evaluation of Forecasts of an Aggregate Using Disaggregated Information

Forecasts for the aggregated food inflation are obtained first from aggregating forecasts of a small number of groups of food items in the CPI basket and second by including lagged information of those disaggregates in the forecasting model for the aggregate. ARX models as well as VARX and VEC models are considered and their forecast ability is evaluated and compared to a traditional benchmark ARX model for the aggregate. Statistical methodologies for time series classification are used to determine the groups of food used to build the forecast of the aggregate in order to reduce collinearity in the second kind of forecast. The results show, that forecasts for the aggregate based on disaggregated information are more accurate than those obtained from a direct model for the aggregate. Forecasts obtained from multivariate models produce minor forecast error that the ones generated by univariate models. On the other hand, for the short term, forecasts for the aggregate constructed aggregating forecasts of the food groups produce minor forecast error that the ones generated by a model that contains not only lags of the aggregate but also lags of the disaggregates. However, for further horizons the later seems better than the former.
Delayed Stochastic Decoding of LDPC Codes

A new stochastic decoding algorithm, called Delayed Stochastic (DS) decoding, is introduced to implement low-density-parity-check (LDPC) decoders. The delayed stochastic decoding uses an alternative method to track probability values, which results in reduction of hardware complexity and memory requirement of the stochastic decoders. It is therefore suitable for fully-parallel implementation of long LDPC codes with applications in optical communications. Two decoders are implemented using the DS algorithm for medium (2048, 1723) and long (32768, 26624) LDPC codes. The decoders occupy 3.93- mm2 and 56.5- mm2 silicon area using 90-nm CMOS technology and provide maximum core throughputs of 172.4 and 477.7 Gb/s at [(Eb)/(No)]=5.5 and 4.8 dB, respectively.
Bounds on fiber minimization in optical networks with fixed fiber capacity

We consider the problem of minimizing the amount of deployed fiber in optical networks in which each fiber carries a fixed number of wavelengths. We are given a network of general topology to carry a set of demands. For each demand we wish to choose a route and a wavelength. Since only distinct wavelengths can be carried on the same fiber, each link e requires max/spl lambda/ Fe(/spl lambda/) fibers where Fe(/spl lambda/) is the number of demands along e that are assigned wavelength /spl lambda/. We wish to minimize the total amount of fiber deployed in order to carry all the demands. Most past work either assumed an unlimited number of wavelengths or else was restricted to specific topologies such as lines, rings and trees. We show that for general topologies the problem is hard to approximate. In particular, for a family of networks of size N, we show that there is no O(log/sup 1/4-/spl epsi//N) approximation algorithm for any /spl epsi/ > 0 unless all problems in NP can be solved by randomized algorithms with expected running time O(n/sup polylog n/). On the positive side we describe methods to choose routes and wavelengths in order to obtain a logarithmic approximation ratio. Lastly we present heuristics that have close-to-optimal performance on example problems on several US backbone networks.