Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiterminal Secrecy by Public Discussion

This monograph describes principles of information theoretic secrecygeneration by legitimate parties with public discussion in the presenceof an eavesdropper. The parties are guaranteed secrecy in the form ofindependence from the eavesdropper's observation of the communication.Part I develops basic technical tools for secrecy generation, many ofwhich are potentially of independent interest beyond secrecy settings.Various information theoretic and cryptographic notions of secrecy arecompared. Emphasis is placed on central themes of interactive communicationand common randomness as well as on core methods ofbalanced coloring and leftover hash for extracting secret uniform randomness.Achievability and converse results are shown to emerge from"single shot" incarnations that serve to explain essential structure.Part II applies the methods of Part I to secrecy generation in twosettings: a multiterminal source model and a multiterminal channelmodel, in both of which the legitimate parties are afforded privilegedaccess to correlated observations of which the eavesdropper has onlypartial knowledge. Characterizations of secret key capacity bring outinherent connections to the data compression concept of omniscienceand, for a specialized source model, to a combinatorial problem of maximalspanning tree packing in a multigraph. Interactive common informationis seen to govern the minimum rate of communication needed toachieve secret key capacity in the two-terminal source model. Furthermore,necessary and sufficient conditions are analyzed for the securecomputation of a given function in the multiterminal source model.Based largely on known recent results, this self-contained monographalso includes new formulations with associated new proofs. Supplementingeach chapter in Part II are descriptions of several openproblems.
Answer Quality Analysis on Community Question Answering

Community Question Answering(CQA) becomes more and more important for information access of Web users.However,CQA content quality varies dramatically from excellence to abuse and spam.This work investigates methods of answer quality analysis on CQA.In particular,it focuses on Baidu Knows,the largest Chinese CQA portal on the Web.A large scale corpus has been constructed by collecting data from the portal,and three new kinds of features were proposed,including sequence-based feature,features in the granularity of question,and BaiduKnows-specific user-based features.To separate high-quality answers from others,a learning based classification method is used to combine the proposed features and traditional textual and link-based features.Experiment results show that the proposed features are effective in improving the performance.Besides,this answer quality analysis framework achieves high accuracy in predicting best answers.
A New Method for Calculating Similarity Between Sentences and Application on Automatic Text Summarization

This paper introduces a new method for calculating similarity between sentences.The algorithm uses not only uni gram but also bi gram and tri gram to calculate similarity.The algorithm is based on regression methods. Experimentations show that the method effective.The final summarization result is better than the algorithm that does not use it.We also propose a new summarization algorithm based on sentences weight and the new sentence similarity calculating method.While extracting the most important sentences,redundancy is also reduced.The evaluation of DUC2003 and DUC2004 shows its effectiveness.Our system rank second among all systems that join in the DUC 2004.
Implementation of K-nearest Neighbor Algorithm Based on GPU

【Abstract】 K-nearest Neighbor(KNN) is a classical problem whose computational complexity increases rapidly with the size of data set. It is an interesting research to accelerate KNN implementation on the Graphics Processor Unit(GPU) by employing GPU’s massive parallel computing power. For its heavy overhead on time,after analyzing the existing work of GPU-based KNN implementations and the architectural features of GPU,this paper efficiently parallelizes KNN on the GPU. It optimizes data access by making good use of the coalesced access power of global memory,and reduces thread serialization by filtering out as much data as possible in advance that is to be sorted. Experiments on KDD,Poker and Covertype datasets and comparisons with some existing methods show that the number of floating point arithmetic of executed per second of this distance computing method is up to 266. 37 × 10,and is up to 26. 47 × 10 in sort phase, which are superior to that of existed methods. 【Key words】 K-nearest Neighbor (KNN) problem;Graphics Processing Unit (GPU); parallel computing; algorithm acceleration;coalesced access;global memory DOI:10. 3969 / j. issn. 1000-3428. 2015. 02. 036
( Cyto ) genomic and epigenetic characterization of BICR 10 cell line and three new established primary human head and neck squamous cell carcinoma cultures

Background Head and neck squamous cell carcinoma cell lines are useful preclinical models to understand the molecular processes underlying the development of such tumors, and to establish targeted therapies. Objective We performed a comprehensive (cyto)genomic and epigenetic characterization of three new established primary human head and neck squamous cell carcinoma cultures and an established, yet undercharacterized cell line: BICR 10. Methods Karyotyping, multiplex fluorescence in situ hybridization, array comparative genomic hybridization and methylation-specific multiplex ligation-dependent probe amplification were applied. Results The three primary cultures turned out to be a near-triploid and BICR 10 near-diploid. Banding and molecular cytogenetic analysis revealed non-random numerical and structural aberrations. The most common rearrangements identified in BICR 10 cell line were non-complex derivatives of reciprocal translocations, in which the breakpoints often appeared in centromeric/near-centromeric regions. In the 3 primary cell cultures the most common rearrangements observed were isoand derivatives chromosomes derived from translocations. Overall, gains of 7p, 8q and losses at 3p, 8p, 9p, 18q and Xp were present in all four studied samples. Among the analyzed genes, BICR 10 cell line exhibited enhanced methylation of gene promoter; however, in all studied samples PAX5, WT1 and GATA5 were methylated. Conclusion The here reported comprehensive characterization of BICR 10 cell line and the new established cultures enriches the resources available for head and neck cancer research, especially for testing therapeutic agents.
On dentability in locally convex vector spaces

For a locally convex vector space (l.c.v.s.) $E$ and an absolutely convex neighborhood $V$ of zero, a bounded subset $A$ of $E$ is said to be $V$-dentable (respectively, $V$-f-dentable) if for any $\epsilon>0$ there exists an $x\in A$ so that $$x\notin \bar{co} (A\setminus (x+\epsilon V)) $$ (respectively, so that $$ x\notin {co} (A\setminus (x+\epsilon V))). $$ Here, "$\bar{co}$" denotes the closure in $E$ of the convex hull of a set. We present a theorem which says that for a wide class of bounded subsets $B$ of locally convex vector spaces the following is true: $(V)$ every subset of $B$ is $V$-dentable if and only if every subset of $B$ is $V$-f-dentable. The proof is purely geometrical and independent of any related facts. As a consequence (in the particular case where $B$ is complete convex bounded metrizable subset of a l.c.v.s.), we obtain a positive solution to a 1978-hypothesis of Elias Saab (see p. 290 in "On the Radon-Nikodym property in a class of locally convex spaces", Pacific J. Math. 75, No. 1, 1978, 281-291).
Prediction of Driver Deceleration Intent in Vehicle Platoon System

This paper proposed the unscented Kalman filter (UKF) in which the state equations are defined by trend, car-following and artificial neural network models to estimate the headway and velocity of platooning vehicles. Based on the estimates, the authors then attempt to predict the driver deceleration intent using a conventional car-following model. The UKF integrated with the car-following model is the best among the other models, but it still needs some improvement to maximize the prediction accuracy.
Study on HDFS improvement scheme based on the GE code and dynamic replication strategy

There is a lot of valuable information in the massive amounts of data. Any loss of data may result in a great loss. Data security cannot be ignored. There are varieties of data disaster recovery technologies. However, most of these techniques depend on the hardware devices or data redundancy greatly. This paper presents a distributed data disaster recovery technology that minimum dependence on data redundancy and hardware system redundancy. In addition, this technology has nothing to do with the user equipment and application data structures. The test proved that this new data disaster recovery method can not only enhance disaster recovery capabilities and reduce the redundancy of the system greatly, but also suitable for large-scale distributed data disaster recovery.
Learning About New Demands in Schools: Considering Algebra Policy Environments (LANDSCAPE)

The past three decades in mathematics education policy and research have seen a considerable focus on algebra and its place in secondary schools. Access to and success in the first high school course, most often termed Algebra I, has been framed as a civil rights issue, a harbinger of college success, and a lynchpin to global competitiveness. Policymakers have targeted access to Algebra I through universal enrollment policies at specific grade levels, while educational researchers and policy analysts have investigated the efficacy of such policies in improving student outcomes. Disciplines Educational Assessment, Evaluation, and Research | Educational Methods | Teacher Education and Professional Development Comments View on the CPRE website. This report is available at ScholarlyCommons: http://repository.upenn. edu/cpre_researchreports/86
Data mining for large datasets: intelligent sampling and filtering

Data Mining and knowledge Discovery has emerged as one of the most promising areas for research over the past decade. However in many real world problems, mining algorithms have access to massive amounts of data. There are two fundamental challenges of dealing with these datasets. The first one is to reduce the amount of time it takes for the mining algorithm to execute, given the size of the dataset as the input. The second challenge is to improve the performance of the learner in terms of assigning unseen instances to a known class or set, based on feature values. The first part of our research shows that mining all the available data is prohibitive dale to computational (time and memory) constraints. Much of the current research is concerned with scaling up data mining algorithms (i.e. improving on existing mining algorithms for larger datasets) to reduce the running time. An alternative approach is to scale down the data. This part of the thesis addresses one approach for scaling down data namely intelligent sampling and applies this solution to the classic problem of handling large datasets. We study and characterize the properties of learning, curves, integrate them with bounds such as Chernoff and Chebyshev in an effort to determine the smallest sufficient dataset size that obtains approximately the same accuracy as the entire available dataset. This part of research focuses on selecting how many (sampling) instances to present to the miming algorithm. The second part of our work aims at improving the performance of the underlying learner for large datasets by eliminating noisy instances in the training set in order to build simpler models, and also to improve the predictive accuracy of the learned model. Many techniques in the literature have been proposed to address this problem. However, a clear understanding of why they work and what computation is being formed is missing. In this area of research, we first analyze how eliminating noisy, instances leads to simpler and more accurate models. A Bayesian analysis is then preformed over three prominent existing filtering approaches. Insights into understanding these other techniques are used as a basis for presenting our novel general purpose noise handling framework.
Fast Bayesian techniques for attenuation corrected whole-body PET

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. To ensure non-negativity of the solution a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15-25 iterations. Reconstructions are presented of an /sup 18/FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors. <<ETX>>
Towards Expertise Modeling Using Hierarchical Classification and Wikipedia Knowledge

xi List of Abbreviations Used . xii Acknowledgements . xiii Chapter
Research on Image Processing Based on SET

This paper presents a hardware implementation of CNN based on SET. Then a CNN and its application in image processing are built and studied. The computer simulation results show that the designed circuits are suitable for CNN implementation because of simple structure, low power dissipation, good frequency property. The model designed makes it convenient and flexible in image processing.
Analysis Of (2,2) Visual Cryptographic Scheme

Visual Cryptographic schemes are one of the secret sharing schemes where a secret in the form of image is encoded into required number, n of shadow images called shares and distributed among n participants. This secret can be revealed 'visually only when these shares are stacked together one on the other. Secret sharing process is a very secured process as the secret is highly maintained . Depending on the algorithm used, the storage, the resolution and the contrast of the reconstructed image will change. This paper is a study of a (2,2) visual cryptographic scheme and the analysis of secrecy, resolution and contrast in the original and stacked image. Though the recover of the secret can be done without using cryptographic techniques and complex algorithms , an effort is made to restore back the quality of the stacked image. This will be useful when scanned documents are transmitted as shares and can be recovered back conveniently. (3) have constructed the secret sharing techniques to strengthened this area of cryptography. In 1994 Moni Naor and Adi Shamir introduced a new concept of secret sharing ,the Visual cryptography, for the encryption and decryption of images or text,without any cryptographic complex computations . The human visual system can decipher the original text by stacking up the shares in the form of transparancies, where a transparency serves as the secret key and other(s) as the cipher text. The individual transparancies look like random looking dot patterns. But only the stacked transparencies reveals the secret which lacks in resolution and contrast compared with the original image. This paper is organized as follows Section (II) deals with the related work and Section (III) deals with the problem and its implementation ,Section(IV) performance analysis and Section (V) contains the conclusion.
Étude et développement d'un système expert basé sur les réseaux de neurones pour le diagnostic des défauts de roulements

En se basant sur l’hypothèse de la présence d’un seul défaut localisé, le but de cette recherche est de décrire un système expert, qui, à partir des descripteurs temporels et fréquentiels issus d’un signal vibratoire mesuré sur un roulement endommagé, est capable de remonter à la source du défaut pour prédire sa gravité ainsi que son emplacement. La première originalité de cette recherche est non seulement de signaler la présence d’un défaut, mais aussi de localiser son emplacement et de lui attribuer une criticité en déterminant son diamètre. La deuxième originalité réside dans la manière que l’apprentissage du réseau de neurones a été fait. En effet, un nombre considérable de données provient d’un simulateur de défaut de roulement appelé BEAT (Bearing Toolbox), qui, une fois calibré par rapport à des mesures expérimentales réelles, permet de générer une base de données d’apprentissage très grande assurant un apprentissage adéquat et couvrant un large éventail de configurations de défauts. La précision du système expert développé dans les configurations retenues est de l’ordre de 99.8% quand à la détermination du diamètre du défaut, et de 97% quand à sa localisation.
IMPLEMENT OF PROBABILISTIC PRODUCTION COST SIMULATION ALGORITHM BASED ON SEQUENCE OPERATION THEORY

A detailed calculation procedure of probabilistic production cost simulation (PPCS) is proposed in this paper. The main idea of this approach is that the process of production simulation is treated as supply-demand matching. This process can be performed under both load duration curve and chronolo-gical curve. Finally, some reliability and economic indices for each individual resource and demand can be calculated based on the theory of expected value of probabilistic sequences. Moreover, some well known conclusions which are got by traditional PPCS can be easily verified by the new approach in this paper. When dealing with traditional PPCS problems, such as IEEE Reliability Test System, the numeric example shows that the result of our approach is same as that of traditional one. However, the new method can meet the new challenge of production cost simulation in IRP or under deregulation environment, which can be shown in the results of case study.
Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming

Deforestration is one of the crucial issues in Indonesia because now Indonesia has world's highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process. Keywords : Deforestration, Multispectral images, landsat, automatic clustering, K-means.
MENTAL MODELS IN HUMAN-COMPUTER INTERACTION

What Is a Mental Model? 40 Mental Models of Text and Other Artifacts . 43 Instructions for Mental Models . 45 Multimedia Instruction . 45 The Theory of Learning by Not Doing . 46 Shared Mental Models . 46 References . 50
Text Mining of Biomedical Literature Repositories

There is an increasing interest in the development of biomedical text mining applications not only to enable improved literature search, but also to automatically detect pointers between biologically relevant entities described in articles and their corresponding records in existing annotation databases. The rapid growth of natural language data in biomedical sciences (including scientific articles, patents, patient records, database textual descriptions) together with the practical relevance of these resources for the design, interpretation and evaluation of bioinformatics and experimental research resulted in the implementation of a considerable number of new applications. For the development and maintenance of manually annotated database, text mining assisted literature duration has been especially promising, as well as for the construction of gold standard datasets and gene lists in the context of Systems Biology and gene set enrichment. Attempts have been made also to integrate text mining with other bioinformatics data such as sequence, structural and gene expression information. We plan to focus primarily on applications of text mining and issues in building text mining systems. We will begin with gentle introduction to text mining and its application in various Biology and Bioinformatics related domains. Existing resources for building text mining applications will be presented in terms of (1) useful data collections, (2) lexical resources, (3) features of natural language data that can be exploited by text mining systems and (4) data mining and natural language processing systems. Also the main types of currently available text mining applications will be discussed, including the retrieval and classification of articles, the identification of mentions of biological entities such as genes, proteins and cell types and the extraction of functional descriptions or protein interaction. The use of literature for knowledge discovery and hypothesis generation will be described. A crucial aspect of literature mining systems is evaluation and usability; these two aspects will be covered trough recent community evaluation efforts such as the BioCreative challenge and the BioCreative metaserver initiative. In order to show what kind of queries and results are currently supported by text mining and information extraction systems, practical example cases will be illustrated in detail, complementing the previously introduced basic descriptions of the underlying methodology. Finally a practical case study will show the step by step implementation of a text mining system illustrating how it is possible to construct such a system for a particular information need. After the tutorial, the participants should be aware of the importance of the biomedical literature as a central data and information source for biology and bioinformatics. They should be able to understand how existing text mining systems work and on what features they rely. Participants would have an overview of currently available tools and how to construct such an application in practice.