Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Evaluation of Software Methodology

Abstract : This paper presented a paradigm for evaluating software development methods and tools. The basic idea is to generate a set of goals which are defined into quantifiable questions which specify metrics to be collected on the software development and maintenance process and product. These metrics can be used to characterize, evaluate, predict and motivate. They can be used in an active as well as passive way by learning from analyzing the data and improving the methods and tools based upon what is learned from that analysis. Several examples were given representing each of the different approaches to evaluation. Additional keywords: Software measurement; Data acquisition; Models. (Author)
Implementing the MALIMET thesaurus on TRIP/TDBS

This paper describes the implementation of Mini-MALIMET (a conventional thesaurus available on magnetic tape) as a searching thesaurus for CLEAR, RETRIEVE and EMSCOPES, (Hoffmann-La Roche Inc. Bibliographic Databases) on TRIP/TDBS text architecture database management system. Topics discussed include: the role of thesauri in information retrieval; the influence of vocabulary on performance of information retrieval systems; user needs; and the enhancement of conventional thesauri for searching of natural language databases. Illustrations in tabular form and an extensive bibliography are included
A computational method to estimate sparce multiple Gaussian graphical models

In recent years several researchers have proposed the use of the Gaussian graphical model defined on a high dimensional setting to explore the dependence relationships between random variables. Standard methods, usually proposed in literature, are based on the use of a specific penalty function, such as the L1-penalty function. In this paper our aim is to estimate and compare two or more Gaussian graphical models defined in a high dimensional setting. In order to accomplish our aim, we propose a new computational method, based on glasso method, which lets us to extend the notion of p-value.
Polarization image fast fusion method based on online dictionary learning

In this paper we propose a polarization image fast fusion approach based on online dictionary learning for sparse non-negative matrix factorization, aiming at improving the efficiency of fusion methods for polarization image based on non-negative matrix factorization. Firstly, all of the polarization parameter images are taken as source data sets for sparse non-negative matrix factorization using online dictionary learning algorithm, so as to extract three feature basis images. Then, after histogram matching, the three feature basis images are mapped into three color channels of IHS color space. Finally, the fused image is achieved via the transform from IHS to RGB color model. Experimental results show that, the proposed method not only has better capacity of color representation capability and effectively pop out detailed information of objects but enhances the running efficiency evidently as well.
Research of Feature Extraction Based on Improved LBP

This paper presents an improved Local Binary Pattern (LBP) operator for feature extraction f which considers both sign and magnitude information of the local difference of neighborhood and center pixels. The image is first divided into small blocks from which improved LBP histograms are extracted and concatenated into a single feature histogram. Then, the Principal Component Analysis (PCA) method is used to reduce feature dimensions. Finally, the recognition is performed by a nearest-neighbor classifier with Chi square statistic as the dissimilarity measurement. Experiments on AR face image databases by the leave-one-out (LOO) procedure illustrate that this method has higher recognition rate and more robust than the original LBP.
A Practical Algorithm for Plagiarism Detection Based on Search Engine

In the current background of quantitative academic evaluation system in China, many scholars, graduate students are tend to plagiarize from web. To detect plagiarism efficiently, there should be a massive text collection which could be accessed easily, cheaply and quickly. Some algorithms refer to the quickly developing online database, such as Chinese CNKI database. We introduced an algorithm to detect plagiarism quantitatively based on natural language segment and precise retrieval function of search engine. The source text is segmented into sentences with punctuation marks. Each sentence is searched in search engine as a single keyword with quotes. The similarity between source file and web information is computed by the ratio of matched sentences return by search engine. The experiments show that this algorithm is practical and feasible.
The R Package bgmm: Mixture Modeling with Uncertain Knowledge

Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subset of observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package called bgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model.
Statistical Analysis Of Emotion Detection Using Fundamental Frequency

— During expressive speech, the voice is enriched to convey not only the intended semantic message but also the emotional state of the speaker. The pitch contour is one of the important properties of speech that is affected by this emotional modulation. This paper presents an analysis of the statistics derived from the pitch contour. First, pitch features derived from emotional speech samples are compared with the ones derived from neutral speech, by using symmetric Kullback-Leibler distance. Then, the emotionally discriminative power of the pitch features is quantified by comparing nested logistic regression models. The results indicate that gross pitch contour statistics such as mean, maximum, minimum, and range are more emotionally prominent than features describing the pitch shape. Also, analyzing the pitch statistics at the utterance level is found to be more accurate and robust than analyzing the pitch statistics for shorter speech regions (e.g., voiced segments). Finally, the best features are selected to build a binary emotion detection system for distinguishing between emotional versus neutral speech. A new two-step approach is proposed. In the first step, reference models for the pitch features are trained with neutral speech, and the input features are contrasted with the neutral model. In the second step, a fitness measure is used to assess whether the input speech is similar to, in the case of neutral speech, or different from, in the case of emotional speech, the reference models. The proposed approach is tested with four acted emotional databases spanning different emotional categories, recording settings, speakers and languages.
Evaluating the Implementation Fidelity of Technology Immersion and Its Relationship with Student Achievement

In a pilot study of the Technology Immersion model, high-need middle schools were “immersed” in technology by providing a laptop for each student and teacher, wireless Internet access, curricular and assessment resources, professional development, and technical and pedagogical support. This article examines the fidelity of model implementation and associations between implementation indicators and student achievement. Results across three years for 21 immersion schools show that the average levels of school support for Technology Immersion and teachers’ Classroom Immersion increased slightly, while the level of Student Access and Use declined. Implementation quality varied across schools and classrooms, with a quarter or less of schools and core-content classrooms reaching substantial implementation. Using hierarchical linear modeling, we found that teacher-level implementation components (Immersion Support, Classroom Immersion) were inconsistent and mostly not statistically significant predictors of student achievement, whereas students’ use of laptops outside of school for homework and learning games was the strongest implementation mediator of achievement.
Interactive television: new genres, new format, new content

The aim of this paper is to discuss some of the main issues associated with interactive genres, formats and content in the context of interactive television (ITV). First, a set of new forms or categorizations of ITV will be presented. Second, the suite of interactive genres, formats and applications that currently constitutes ITV will be introduced and discussed. And third, some general conclusions concerning interactivity, television and the interactive user/viewer will be drawn.
Fault Trajectories and Location Estimations for Distance Protection using two New Simplified Phasor Analysers

Abstract The paper presents two new computationally simple algorithms for the protection of power transmission lines during fault conditions. The first method is based on the minimisation of a weighted forgetting factor based error cost function by the use of a fast Gauss-Newton method approach. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, which is immune to random noise, waveform distortion due to DC offsets and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time apparent impedance estimation. The second approach is based on a phase corrected wavelet transform known as fast discrete S-transform, which unlike the conventional one, uses frequency selection and frequency localised band pass filtering techniques to reduce the computational overhead and makes it suitable for on-line implementation in computing the fault location on an overhead transmission line. Both the proposed schemes are evaluated for the protection of a transmission line in a meshed power system network for a variety of fault types, fault locations, fault resistances, and significant noise in the signal using computer simulation studies.
Sub-band VAD Based on Continuous Noise Estimation in Wavelet Domain

Voice Activity Detectors (VADs) are widely used in speech processing applications such as speech enhancement and coding. In speech enhancement systems, we use VAD in order to obtain more accurate estimation of noise. Voice activity detection methods usually work in time or frequency domains. In this paper, we propose two approaches for voice activity detection in wavelet domain for continuous noise estimation: Subband VAD and Full-band VAD. We use the correct noise/speech classification rate to show the acceptable performance of our proposed VADs. In addition, we apply sub-band VAD to a speech enhancement system. Objective and subjective measures show that Sub-band VAD outperforms Fullband VAD in noise reduction applications. Streszczenie. Analizowano detektor aktywności glosowej VAD w zastosowaniu do poprawy jakości dźwieku mowy. Celem bylo określenie szumow towarzyszących. Zastosowano transformate falkową do analizy szumow. (Określanie szumow przy wykorzystaniu transformaty falkowej i detektora VAD)
A RECONFIGURABLE AND SCALABLE EFFICIENT ARCHITECTURE FOR AES

A new 32-bit reconfigurable FPGA implementation of AES algorithm is presented in this thesis. It employs a single round architecture to minimize the hardware cost. The combinational logic implementation of S-Box ensures the suitability for non-Block RAMs (BRAMs) FPGA devices. Fully composite field GF((24)2) based encryption and keyschedule lead to the lower hardware complexity and convenience for the efficient subpipelining. For the first time, a subpipelined on-the-fly keyschedule over composite field GF((24)2) is applied for the all standard key sizes (128-, 192-, 256-bit). The proposed architecture achieves a throughput of 805.82Mbits/s using 523 slices with a ratio throughput/slice of 1.54Mbps/Slice on Xilinx Virtex2 XC2V2000 ff896 device.
Curating and Searching the Annotated Web

We demonstrate CSAW, a system for Curating and Searching the Annotated Web. CSAW annotates named entities and quantities in Web-scale text corpora, and, where confident, connects these annotations with entries in an entity and type catalog such as Wikipedia. The semistructured catalog, together with the unstructured corpus, forms a composite database that CSAW can then search using powerful reachability, proximity and aggregation primitives. Specifically, we can look for snippets with mentions of specific entities, entities of a specified type, quantities with specified types or units, find unions and intersections of snippet sets, and then aggregate evidence from snippet sets into ranked responses. Responses are not page URLs as in standard Web search, but ranked tables where the cells can be entity references, quantities, or token snippets. We will show a subset of CSAW’s capabilities, and describe the beginnings of a next-generation Web search API that significantly extends the capabilities of APIs provided by popular search engines today.
Multi-layer perceptron ensembles for pattern recognition: some experiments

All global optimization methods trying to select the best model for a given task have to face the problem of the bias introduced during learning by several factors (e.g., overfitting problems or local minima) which may lead to poor generalization. Multiple models can reduce this problem by combining the results of a population of differently trained networks. Unfortunately, nonlinear adaptive systems like multilayer perceptrons cannot be integrated in the parameter (weight) space. Therefore, a key issue in the development of efficient network ensembles is the trade-off between performance improvement and the required resource overhead with respect to standard optimal model selection techniques. In this work several approaches to the design of multiple multilayer perceptrons classifiers are compared, and their robustness to some implementation constraints is evaluated. <<ETX>>
An Overview of Deep Generative Models

ABSTRACT As an important category of deep models, deep generative model has attracted more and more attention with the proposal of Deep Belief Networks (DBNs) and the fast greedy training algorithm based on restricted Boltzmann machines (RBMs). In the past few years, many different deep generative models are proposed and used in the area of Artificial Intelligence. In this paper, three important deep generative models including DBNs, deep autoencoder, and deep Boltzmann machine are reviewed. In addition, some successful applications of deep generative models in image processing, speech recognition and information retrieval are also introduced and analysed.
Parallel volume rendering on distributed-memory multiprocessor system

Several approaches have been proposed to visualize 3D volume data on multiprocessor systems in recent years. Most of these algorithms are tailored for special architectures and hard to immigrate to new architectures, such as Tsinghua High-Performance Cluster System (THPCS), a distributed-memory multiprocessor system. In this paper, a parallel volume rendering algorithm is presented for THPCS. To reduce the communication cost and the complexity of blending local intermediate images, a static subtask distribution strategy is proposed. Combined with the prediction for the subtask load and node performance, our strategy yields a good load balance. An asynchronous binary compositing method is adopted to provide flexibility for our algorithm. The experimental results demonstrate the efficiency and practicality of our algorithm. As a result, THPCS using our algorithm achieves performance ofrendering 512 x 512 color image for 128x 128x 197 volume within 1 second.
Tradeoffs of DDoS solutions

Distributed denial of service (DDoS) has become a serious threat to the Internet. Many schemes against DDoS attacks have been proposed, including ingress/egress filtering, IP traceback, authentication, and so on. We focus on tradeoffs of DDoS solutions. Three tradeoffs are considered, the first one is space, complexity, efficiency and robustness tradeoffs of these packet marking schemes; the second one is marking probability of node sampling scheme, the third one is timeout period of server for three-hand-shaking. Two schemes are suggested, one is combining node append scheme with node sampling scheme, the other is setting SYN timeout dynamically. Proper tradeoffs can be made using these schemes.
Combined turbo equalization and turbo decoding

In this paper, the subject of turbo coding in the presence of intersymbol interference channel is investigated. An iterative decoder structure is presented, which combines the channel equalization and the turbo decoding. At each iteration extrinsic information from the channel detector is fed into the turbo decoders, and then their extrinsic information is fed back to the channel detector. Simulation results are presented for turbo code with BPSK and and PSK modulations, transmitted over several ISI channels having severe frequency distortion. The performances are about 1 dB from the ISI channel capacity at bit error rate of 10/sup -5/.
Design Of Low Error And Power Fixed Width Multiplier By Using Compensation Function

In this paper, a new error-compensation network for fixed-width multiplier is proposed. The error compensation block is composed of dual trees which are optimally chosen in order to minimize either the meansquare error or the maximum absolute error. The new technique significantly improves error performance with respect to previous approaches. Simulation results show that new fixed-width multipliers exhibit significant improvements both in mean square error and in power dissipation with respect to previous solutions. As compared with the state-of-the-art , the proposed fixedwidth multiplier performs not only with lower compensation error but also with lower hardware complexity, especially as multiplier input bits increases.
Combination of Data

A general discussion is given of the statistical issues involved in merging information from different sets of data, a common parameter of interest being involved. In the simplest situation, a weighted mean of separate estimates is used, care being needed particularly when the weights themselves are subject to appreciable errors of estimation. The possible impact of systematic errors is discussed. The latter parts of the section concern more complicated situations. Keywords: synthesis of information; weighted means; systematic errors
Design techniques for clocking high performance signaling systems

approved: Un-Ku Moon Scaling of CMOS technology has progressed relentlessly for the past several decades. In order for this unprecedented scaling to benefit the performance of large digital systems, the communication bandwidth between integrated circuits (ICs) must scale accordingly. However, interconnect technology does not scale as aggressively, making communication between chips the major bottleneck in overall system performance. In addition, supply voltage scaling, increasing device leakage, and increased noise make existing signaling circuits inefficient and difficult to scale. In this thesis, both analog and digital enhancement techniques to mitigate scaling related issues and improve the performance of building blocks used in highspeed signaling systems are discussed. A digital-to-phase converter (DPC) with a resolution better than 100 femto-second resolution, a hybrid analog/digital clock and data recovery (CDR) architecture that improves the tracking range of traditional CDRs by an order of magnitude, and a digital CDR architecture that obviates the need for the charge pump and the large area occupying loop filter, while achieving error-free operation are presented. Measured results obtained from the prototype chips are presented to illustrate the proposed design techniques. c ©Copyright by Pavan Kumar Hanumolu August 21, 2006 All Rights Reserved Design Techniques for Clocking High Performance Signaling Systems by Pavan Kumar Hanumolu A DISSERTATION submitted to Oregon State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy Presented August 21, 2006 Commencement June 2007 Doctor of Philosophy dissertation of Pavan Kumar Hanumolu presented on August 21, 2006
Remotely sensed image processing service composition based on heuristic search

As remote sensing technology become ever more powerful with multi-platform and multi-sensor, it has been widely recognized for contributing to geospatial information efforts. Because the remotely sensed image processing demands large-scale, collaborative processing and massive storage capabilities to satisfy the increasing demands of various applications, the effect and efficiency of the remotely sensed image processing is far from the user's expectation. The emergence of Service Oriented Architecture (SOA) may make this challenge manageable. It encapsulate all processing function into services and recombine them with service chain. The service composition on demand has become a hot topic. Aiming at the success rate, quality and efficiency of processing service composition for remote sensing application, a remote sensed image processing service composition method is proposed in this paper. It composes services for a user requirement through two steps: 1) dynamically constructs a complete service dependency graph for user requirement on-line; 2) AO* based heuristic searches for optimal valid path in service dependency graph. These services within the service dependency graph are considered relevant to the specific request, instead of overall registered services. The second step, heuristic search is a promising approach for automated planning. Starting with the initial state, AO* uses a heuristic function to select states until the user requirement is reached. Experimental results show that this method has a good performance even the repository has a large number of processing services.
Traffic flow time series prediction based on statistics learning theory

For intelligent transportation systems, a new traffic flow time series prognostication is proposed in this paper. Compared with classical methods, support vector machine has a good generalize ability for limited training samples, which has a characteristic of rapid convergence and avoiding the local minimum. At the end of this paper, the simulation experiment for the traffic flow of one practice crossing proves the validity and efficiency and high application value in traffic flow prediction.