Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving Toward Synergy

This study examines the process of media supplementation in the Stanford Five-City Multifactor Risk Reduction Project, a long-term trial of communitywide cardiovascular disease risk reduction conducted by Farquhar and associates. The data indicate that accessible awareness media disseminated early in the education campaign promoted use of information-rich depth media later on, demonstrating that supplementation occurred. Moreover, analyses suggest that awareness media prompted behavioral trials and information seeking through interpersonal discussion and print media. Results further imply that this information and support encouraged behavior change and enhanced self-efficacy. It is argued that the ability of a long-term, multifaceted campaign to promote complex interactions between media messages, interpersonal discussion, and individuals' knowledge, attitudes, and behavior over time is the strength of this kind of project—in other words, the possibility for synergy.
Initiatory and Reactive System Roles in Human Computer Discourse

CUCS-1Sl-8S User interfaces for a variety of computer systems will need to develop more conversational abilities in order to comrrunicate effectively and cooperatively with the user. In particular, such systems need to be able to shift flexibly from asking questions and making requests of the user to answering similar questions and requests from the user. We provide an analysis of the discourse-level, linguistic phenomena involved in such shifts. We further specify the infonnation such an automated system would need to maintain to support this linguistic capability and how it would use the information. We conclude by showing output from an automated student advising program ADVISOR exemplifying a specific case of taking the initiator role to provide unrequested information. lThis work was partially supported by ONR grant N00014-82-k-0256 and by AT&T Bell Laboratories. 600 Mountain Ave., Murray Hill, NJ 07974 Table of
Research and application of active noise control system in vehicle

With the improvement of living standard, riding comfort has become an important indicator of choosing a car, but the noise in the car is the key factor affecting the ride comfort. The traditional passive noise reduction technology has a good inhibitory effect on high frequency noise, but the effect of low frequency noise is not ideal, and the active noise control system solves this problem well. This article first analyzes the characteristics and components of automobile noise, this paper introduces the principle of active noise control system, introduces the structure of the feed-forward and feedback structure and mixed structure of active noise control system structure characteristics and performance, and analyzes its feasibility for automobile noise suppression. The minimum mean square (LMS) algorithm and the filtering X minimum mean square (FX-LMS) algorithm are introduced. Based on DSP development platform, the identification algorithm of the secondary acoustic channel and the ANC control algorithm are completed in the one-dimensional sound field. The noise source includes narrowband noise and broadband noise. Set up a single channel in the test vehicle. The experiment results show that the system has certain control effect on the broadband noise.
Design, Analysis, And Computational Methods For Engineering Synthetic Biological Networks

This thesis advances our understanding of three important aspects of biological systems engineering: analysis, design, and computational methods. First, biological circuit design is necessary to engineer biological systems that behave consistently and follow our design specifications. We contribute by formulating and solving novel problems in stochastic biological circuit design. Second, computational methods for solving biological systems are often limited by the nonlinearity and high dimensionality of the system’s dynamics. This problem is particularly extreme for the parameter identification of stochastic, nonlinear systems. Thus, we develop a method for parameter identification that relies on data-driven stochastic model reduction. Finally, biological system analysis encompasses understanding the stability, performance, and robustness of these systems, which is critical for their implementation. We analyze a sequestration feedback motif for implementing biological control. First, we discuss biological circuit design for the stationary and the transient distributional responses of stochastic biochemical systems. Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture their dynamics. The chemical master equation is a commonly used stochastic model that describes how the probability distribution of a chemically reacting system varies with time. Here we design the distributional response of these stochastic models by formulating and solving it as a constrained optimization problem. Second, we analyze the stability and the performance of a biological controller implemented by a sequestration feedback network motif. Sequestration feedback networks have been implemented in synthetic biology using an array of biological parts. However, their properties of stability and performance are poorly understood. We provide insight into the stability and performance of sequestration feedback networks. Additionally, we provide guidelines for the implementation of sequestration feedback networks. Third, we develop computational methods for the parameter identification of stochastic models of biochemical reaction networks. It is often not possible to find analytic solutions to problems where the dynamics of the underlying biological circuit are stochastic, nonlinear or both. Stochastic models are often challenging due to their high dimensionality and their nonlinearity, which further limits the availability of analytical tools. To address these challenges, we develop a computational method for data-driven stochastic model reduction and we use it to perform parameter identification. Last, we provide concluding remarks and future research directions.
Scheduling parallel dedicated machines with the speeding‐up resource

We consider a problem of scheduling jobs on m parallel machines. The machines are dedicated, i.e., for each job the processing machine is known in advance. We mainly concentrate on the model in which at any time there is one unit of an additional resource. Any job may be assigned the resource and this reduces its processing time. A job that is given the resource uses it at each time of its processing. No two jobs are allowed to use the resource simultaneously. The objective is to minimize the makespan. We prove that the two‐machine problem is NP‐hard in the ordinary sense, describe a pseudopolynomial dynamic programming algorithm and convert it into an FPTAS. For the problem with an arbitrary number of machines we present an algorithm with a worst‐case ratio close to 3/2, and close to 3, if a job can be given several units of the resource. For the problem with a fixed number of machines we give a PTAS. Virtually all algorithms rely on a certain variant of the linear knapsack problem (maximization, minimization, multiple‐choice, bicriteria). © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008
The stochastic U‐line balancing problem

A U‐line arranges tasks around a U‐shaped production line and organizes them into stations that can cross from one side of the line to the other. In addition to improving visibility and communication between operators on the line, which facilitates problem‐solving and quality improvement, U‐lines can reduce the total number of operators required on the line and make rebalancing the line easier compared to the traditional, straight production line. This paper studies the (type 1) U‐line balancing problem when task completion times are stochastic. Stochastic completion times occur when differences between operators cause completion times to vary somewhat and when machine processing times vary. A recursive algorithm is presented for finding the optimal solution when completion times have any distribution function. An equivalent shortest path network is also presented. An improvement for the special case of normally distributed task completion times is given. A computational study to determine the characteristics of instances that can be solved by the algorithms shows that they are able to solve instances of practical size (like the 114 Japanese and U.S. U‐lines studied in a literature review paper). © 2002 Wiley Periodicals, Inc. Naval Research Logistics, 2003
A Kind of ECC-Homomorphism Key Agreement in Grid

Making use of the Sander and Tschudin defined additive-multiplicative homomorphism in the integer ring and ECC, a new protocol, called the ECC-homomorphism key agreement, is presented. It can create the contributory, forward secrecy session key in grid. The security of the protocol is proved by BAN logic.
Computational annotation of protein function

The term biological function of a protein is a little bit ambiguous. The exact meaning of this term varies and depends upon the context in which it is used. The term protein function is a broader term 3 and has more than one aspect. There are several computer-based tools which can be used for in silico prediction of protein function just out of protein sequence. While using a protein function prediction tool, it is important to consider which functional aspect is to be predicted.4
A Network Approach to Learning Supervised Topic Model with Word Embedding

Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. Moreover, we describe a simple way to train the model that is well suited to a semi-supervised setting where we only have supervisory signals for some portion of the corpus and the goal is to improve prediction performance in the held-out data. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of both held-out document classification tasks and topic coherence.
A stochastic gradient algorithm for transmit antenna weight adaptation with feedback

It is generally accepted that the downlink of next generation cellular systems will require greater capacity than the uplink, due to the asymmetry of data traffic patterns. The use of transmit adaptive antenna arrays at the base station is a promising area for downlink capacity improvement. This paper describes a gradient algorithm utilizing mobile to base feedback in order to achieve some of those possible gains. A simple algorithm for adaptation of the complex baseband weights of a transmit antenna array utilizing feedback from the receiving unit is presented. A dedicated pilot signal is transmitted from the transmitting unit with a pair of perturbed test weights; a feedback bit is transmitted from the receiving unit indicating which weight vector provided the larger power; the transmitting unit adjusts a base weight to this selection and continues the process. This approach is shown to be a coarse stochastic gradient algorithm. The settling time and steady state characteristics of the algorithm are investigated analytically and verified by simulation, and the algorithm is shown to converge to those weights which maximize the power delivered to the receiver.
Comprehensive review of IDMs in DG systems

Distributed generation (DG) offers solution to the ever increasing energy needs by generating energy at the consumer end, in most cases, by means of renewable energy sources. Islanding detection is an important aspect of interconnecting a DG to the utility. This study presents comprehensive review of various islanding detection techniques along with their relative advantages and disadvantages. A broad classification of islanding detection methods (IDMs) is laid out as classical methods, signal processing (SP)-based methods, and computational intelligence-based methods with a focus on SP-based methods and computational intelligence-based methods. The evolution of SP techniques used for islanding detection is presented along with the merits and shortcomings of each technique. Furthermore, the advent of computational intelligence methods based IDMs are discussed along with their merits and demerits. An insight into various islanding methods based on quantitative measures of performance indices such as detection time, detection accuracy, and efficiency are tabulated and presented. Finally, the prospective direction of research for IDMs is also presented.
A New Approach to the Detection of Mammogram Boundary

Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
Minimum error training for speech recognition

In recent years several research groups have investigated the use of a new framework for minimizing the error rate of a classifier. The key idea is to define a smooth, differentiable loss function that incorporates all adaptable classifier parameters and that approximates the (non-smooth) actual performance error rate. This framework is applicable to a variety of classifier structures, including feedforward neural networks, learning vector quantization classifiers, and hidden Markov models. Here we describe a particular application in which a relatively simple distance-based classifier is trained to minimize errors in speech recognition tasks. The loss function is defined so as to reflect errors at the level of the final, grammar-driven recognition output. We show how the loss function can be made to reflect not just correctness/incorrectness at the string level, but also, for instance, a word spotting loss between the recognized string and the correct string. Thus, minimization of this loss can explicitly optimize the word spotting rate. <<ETX>>
Distributed coding and computing for networks

This work is dedicated to the design, analysis, and the performance evaluation of new coding schemes suitable for distributed storage systems. The first part is devoted to spatially coupled codes for erasure channels. A new method of spatial coupling for low-density parity-check ensembles is proposed. The method is inspired from overlapped layered coding. Edges of local ensembles and those defining the spatial coupling are separately built. We also propose to saturate the whole Root-LDPC boundary via spatial coupling of its parity bits to cope with quasi-static fading. Then, spatial coupling is applied on a Root-LDPC ensemble with double diversity designed for a channel with 4 block-erasure states. In the second part of this work, we consider non-binary product codes with MDS components and their iterative row-column algebraic decoding on the erasure channel. Both independent and block erasures are considered. A compact graph representation is introduced on which we define double-diversity edge colorings via the rootcheck concept. Stopping sets are defined and a full characterization is given in the context of MDS components. A differential evolution edge coloring algorithm that produces colorings with a large population of minimal rootcheck order symbols is presented. The performance of MDS-based product codes with and without double-diversity coloring is analyzed in presence of both block and independent erasures. Furthermore, numerical results show excellent performance in presence of unequal erasure probability due to double-diversity colorings.
A Review of Sequential Changepoint Detection in Quality Control and Dynamical Systems

This thesis introduces concepts of control chart and its operation, and review various control charts that have been suggested: Shewhart’s X̄ chart, exponentially weighted moving average (EWMA) chart, cumulative sum (CUSUM) chart, generalized likelihood ratio (GLR) chart and mixture liklihood ratio chart using integrated likelihood ratio. Then, we discuss procedure and advantages of Poisson CUSUM chart. Also, two kinds of parameter change test for Poisson autoregressive models are considered: an estimate-based test using CMLE and a residual-based test. Finally, we perform a simulation study for illustration. We are going to simulate Poisson CUSUM chart with INGARCH(1,1) data if it is usable in spite of the autocorrelation. For the simulation, we deal with various cases of λ, α and β values to see if Poisson CUSUM chart is also valid for autocorrelated Poisson data.
OFDM channel estimation in the presence of interference

We develop a frequency-domain channel estimation algorithm for single-user orthogonal frequency division multiplex (OFDM) wireless systems in the presence of interference. The received measurement is correlated in space with a covariance matrix dependent on frequency. Hence, the commonly used least-squares algorithm is suboptimal. On the other hand, accurate estimation of the spatial covariance matrix in such a model using the multivariate analysis of variance (MANOVA) method would impose significant computational overhead, since it would require a large number of pilot symbols. To overcome these problems, we propose to model the covariance matrix using an a-priori known set of frequency-dependent functions of joint (global) parameters, resulting in a structured covariance matrix. We estimate the interference covariance parameters using a residual method of moments (RMM) estimator and the mean (user channel) parameters by maximum likelihood (ML) estimation. Since our RMM estimates are invariant to the mean, this approach yields simple non-iterative estimates of the covariance parameters while having optimal statistical efficiency. Therefore, our algorithm outperforms the least-squares method in accuracy, and requires a smaller number of pilots than the MANOVA method and thus has smaller overhead. Numerical results illustrate the applicability of the proposed algorithm.
A Novel Algorithm for Automatic Human Face Retrieval in Video

Huge amount of video data is being generated every day, with enormous growth of security and surveillance system. It is immensely challengeable for researcher to search and retrieve accurate human face of interest from video with utmost speed. The proposed work is stimulated from the same concern. It would be the future demand for searching, browsing, and retrieving human face of interest from video database for several applications. This paper proposes the novel algorithm for human face retrieval from video database based on holistic approach. The Viola and Jones frontal face detector detect the face region. The next stage is face extraction which have input for grouping individual faces. The individual group of faces has converted into single normalized mean face using PCA. The final face group contains single face for each person occurred in video. After the pre-processing of normalized faces, recognition is performed on the basis of query face image. General Terms Computer Vision, pattern recognition, video processing
A complex valued Hebbian learning algorithm

We present a training rule for a single-layered linear network with complex valued weights and activation levels. This network can be used to extract the principal components of a complex valued data set. We also introduce a new training method that reduces the training time of the complex valued as well as of the real valued network. The use of the new network and training algorithm is illustrated with a problem of compressing images represented in the spectral domain.
Data Mining Approach to Herbs Classification

Herbs are one of the high-value products in Malaysia. The term ‘herbs’ has more than one definition. It is also demanding by multiple manifolds. Herbs are used in many sectors nowadays. The ability to identify variety herbs in the market is quite hard without the intervention of human experts. Unfortunately, human experts are prone to error. Herbs classification is able to assist human experts and at the same time minimizing the intervention. This research performs identification and classification of herbs based on image capture ad variety of classification algorithms such as an Artificial Neural Network (ANN), K-Nearest Neighbors (IBK), Decision Table (DT) and M5P Tree algorithms. The selected algorithms are implemented and evaluated to their relative performance and IBK is found to produce the highest quality outputs.
A comprehensive review of honey encryption scheme

We present a comprehensive survey of the Honey Encryption (HE) scheme. Honey Encryption is an encryption scheme that provides resilience against brute-force attack by serving up plausible-looking but fake plaintext for every invalid key used by an intruder to decrypt a message. Our goal is to furnish researchers with the framework of the scheme not just for implementation purpose but to identify the gaps in the scheme and answer the open questions that remain unanswered by the small set of research carried out since its inception. We identified two major open areas which are the difficulty of creating semantically and contextually plausible-looking and convincing decoy message that is good enough to fool the attacker into believing he has the original message. Secondly, typo problem; where a fake plaintext appears valid to a legitimate user when he mistakenly enters a wrong key. Our findings consolidate the need for further research as state-of-the-art research fails to produce convincing decoys that are good enough to keep the attacker from acquiring the message.