Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Systems Thinking methods for resilience research

There is no commonly accepted typology to describe the field of Systems Thinking. It may be viewed from a number of different perspectives. This is interesting as systems and systemic problem situations may themselves be considered as conceivable from a number of different perspectives or “dimensions”. The more complex a systemic situation, the more relevant becomes taking a range of different dimensional views in its analysis. Critical Systems Thinking (CST), a domain within Systems Thinking, supports multi-dimensional analysis of systems and offers approaches to support practitioners in selecting and combining multiple systems thinking methods for this purpose. A detailed review of the Critical Systems Thinking literature and the multiple-systems-thinking-methods approaches therein reveals that there is a problem. Much debate surrounds the validity of the theory upon which these meta-methodologies are founded. According to Zhu (2011), “Combining multiple methodologies works in practice, but not yet in theory. ” The need to find a reliable approach to selecting and combining multiple systems methods for the purpose of resilience research has led to the proposal of a systems-theory-based meta-methodology as an extension to CST. The proposal is a fusion of the multi-methodology ideas of Jackson, Mingers and Brocklesby with ideas for the conceptual dimensions of systems and levels of complexity of Angyal, Emery and Trist, together with the systems archetypes idea of Senge. It is hypothesised that the proposed new meta-methodology is useful for supporting resilience research. The thesis concludes with suggestions for future work required to develop the hypothesis further.
The Pagerank Method for Automatic Detection of Microseismic Events

Summary In microseismic monitoring, the fundamental task is to detect events automatically. The majority of microseismic events may suffer the limits from low signal-to-noise ratio for detection. Using a single master event to correlate the entire dataset may not work because events do not necessarily all exhibit sufficient similarity to the same event. During hydraulic fracturing, however, it is likely that event A may be similar to event B, and event B may be similar to event C, but event A may not be similar to event C on the same standard. The situation of this event connectivity is similar to web pages, in which page content similarity is detected by PageRank. PageRank is one of the initial web page searching algorithms implemented by Google to estimate the links between documents. Applying that technology to microseismic problems, we rank windows of recorded data for similarity measurement, thus detect which windows likely contain potential events. We generate synthetic microseismic events with different source mechanisms, and demonstrate that the approach can handle noise and variations in waveform data reasonably
Geometric matching of 3D objects: assessing the range of successful initial configurations

This paper considers the matching of 3D objects by a geometric approach based on the iterative closest point algorithm (ICP), which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. The algorithm does not converge always to the best solution. It can be trapped in a local minimum and miss the optimum matching. While the convergence of this algorithm towards the global minimum is known to depend largely on the initial configuration of test and model objects, this paper investigates the quantitative nature of this dependence. Considering the space C of relative configurations of the two objects to be compared, we call range of successful initial configurations, or SIC-range, the subspace of C which configurations bring the algorithm to converge to the global minimum. In this paper, we present a frame for analyzing the SIC-range of 3D objects and present a number of original experimental results assessing the SIC-range of a number of real 3D objects.
A Chatbot Dialogue Manager - Chatbots and Dialogue Systems: A Hybrid Approach

Conversational software, that is software with which a user can converse in a natural language such as English or Dutch, can be classified into two distinct categories: chatbots and dialogue systems. Both categories of systems have their advantages and disadvantages. Chatbot systems are trivial to build and maintain, but are too simplistic for applications that do more than answering frequently asked questions. Dialogue systems on the other hand are harder to develop and maintain, can deal with less variety in user input, but are capable of handling and generating all kinds of linguistic phenomena such as grounding and information revision. This thesis presents a system that combines ideas from chatbots and dialogue system. This hybrid system doesn’t parse natural language into a semantic presentation, but contains a dialogue manager that can handle natural language directly. This system would make it easy to implement and maintain new domains even when the developer has little or no knowledge of computational linguistics. A statistics tutor has been implemented using this hybrid system with which three students interacted. This shows that the statistics tutor is able to deal with phenomena such as grounding, question accommodation and information revision.
Data Mining technique and its Application: Association rules mining for Korean language

This paper explains the importance of Association Rules Mining for of Korean language (text). Association rules mining can also be used for mining association rules from textual data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining model calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data and select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.
Single Instance Storage in Windows

Certain applications, such as Windows 2000's Remote Install service, can result in a set of files in which many different files have the same content. Using a traditional file system to store these files separately results in excessive use of disk and main memory file cache space. Using hard or symbolic links would eliminate the excess resource requirements, but changes the semantics of having separate files, in that updates to one "copy" of a file would be visible to users of another "copy. " We describe the Single Instance Store (SIS), a component within Windows ® 2000 that implements links with the semantics of copies for files stored on a Windows 2000 NTFS volume. SIS uses copy-on-close to implement the copy semantics of its links. SIS is structured as a file system filter driver that implements links and a user level service that detects duplicate files and reports them to the filter for conversion into links. Because SIS links are semantically identical to separate files, SIS creates them automatically when it detects files with duplicate contents. This paper describes the design and implementation of SIS in detail, briefly presents measurements of a remote install server showing a 58% disk space savings by using SIS, and discusses other possible uses of SIS.
Optical character recognition without segmentation

A segmentation-free approach for off-line optical character recognition is presented. The proposed method performs the recognition by extracting the characters from the whole word, avoiding the segmentation process. A control point set which includes position and attribute vectors is selected for the features. In the training mode, each sample character is mapped to a set of control points and is stored in an archive which belongs to an alphabet. In the recognition mode, the control points of the input image are first extracted. Then, each control point is matched to the control points in the alphabet according to its attributes. During the matching process, a probability matrix is constructed which holds some matching measures (probabilities) for identifying the characters. Experimental results indicate that the proposed method is very robust in extracting the characters from a cursive script.
Acoustic noise suppression using regressive adaptive filtering

Experimental field tests dealing with the background acoustic noise in cars under various driving conditions are described. Analysis reveals that there is a high correlation between the acoustic noise in the area facing the driver's seat and the noise in other locations in the car, which suggests the possibility of using the two-microphones noise cancellation approach. Results show that using a conventional finite-impulse response adaptive filter with the stochastic gradient adaptation algorithm leads to up to 12 dB of noise cancellation in the low end of the noise spectrum; some noise enhancement was noticed at the high end of the spectrum. This problem is discussed, along with a possible solution approach using proper filtering. A limitation of the two-microphones cancellation is that the optimal location of the secondary microphone varies, depending on the driving conditions. A multiple secondary microphones scheme is proposed as a solution. This scheme resulted in further reductions of the residual noise. <<ETX>>
Wavelet and subband transforms: fundamentals and communication applications

Subband and wavelet transforms have been a subject of great interest, especially in the fields of signal processing and applied mathematics. This article presents a tutorial on this subject, emphasizing the fundamentals and the reason for its success, importance, and potential. Subband and wavelet transform applications in communications are also reviewed. These application areas include interference excision, spread spectrum codes based on subband transform bases, CDMA, and discrete multitone transceivers.
Stability analysis of the turbo decoding algorithm using Max-Log-MAP

In this paper we investigate analytically the convergence properties of the iterative turbo decoding with the Max-Log-MAP algorithm for soft-output generation. With respect to the iterative decoding scheme, several equivalent mathematical formulations are derived to facilitate the stability analysis, based on which we show that turbo decoding with Max-Log-MAP has at least one fixed point but is in general suboptimal and may not even converge. Finally, some conditions are given to guarantee the convergence of the iterative turbo decoding.
Recovering valid clusters with ISODATA supervised by the CAIC

The authors developed an unsupervised clustering method that is a variant of the well known ISODATA clustering algorithm. They replace the heuristic rules that control ISODATA with rules that search for the minimum value of an information theoretic criterion. The criterion investigated in this study is the Consistent Akaike's Information Criterion (CAIC). The CAIC is a measure of the global fit of a cluster model to the input data, and the smallest CAIC value suggests the best fit. The authors tested the method on both multivariate Gaussian and real-world data, including MR (magnetic resonance) images of aortas in vivo. <<ETX>>
Secured Wireless Power Transmission Using Radio Frequency Signal

Anything we transmit without protection is being raid by the intruders. Hence it is necessary to impart security to the signal transmitted. In this paper we present the concept of giving security to the transmitted power signal which is to be received by a receiver in a wireless medium. The present techniques of wireless power transmission consists of a pair of strongly magnetically coupled resonators that allow power transmission of tens of watts over a few meters, but anything without security becomes unreliable. With the goal in achieving security to the power signal, we introduce the concept of encryption decryption algorithm using RF module.
A wavelet multiresolution and neural network system for BCG signal analysis

Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during measurements. This provides a potential application to assess the patients heart condition in the home. Artificial neural networks (ANNs) have several properties that make them promising for the automatic signal classification problems. In the time domain of the BCG classification, the whole cardiac cycle of BCG waveform needs a large size neural network and a large training sample which make the classification a computationally intensive task. By classifying the data in a compressed format, savings in computer time may be realised. In this paper, we used wavelet multiresolution analysis that allows significant information content of the BCG signal to be obtained. Small subsets of the wavelet coefficients were used to classify the normal hypertension and heart attack risk subjects by a single hidden layer neural network. It is shown that the proposed system achieved overall 94.66% correct classification rate for testing the data set. The advantage of the proposed classification system is to reduce the computation complexity and to be easily implemented into a standalone device for real time application.
Rare conditions-an important cause of failures

Data gathered by the author as well as those published by others show that rarely executed code has a much higher failure rate expressed in terms of execution time than frequently executed code during the early operational period. More detailed analysis of the data shows that the inability to handle multiple rare conditions, such as response to hardware failures or exception conditions caused by the computer state, is a prominent cause of program failure in well-tested systems. Approaches to coping with this difficulty are discussed. We investigated differences in failure rate between rarely executed code (RC) and frequently executed code (FC) in the NASA/JPL Deep Space Network (DSN). <<ETX>>
Performance evaluation of modified HPSK for wideband CDMA

In mobile communications, power is a very important factor and non-linear amplification of the power amplifier cannot be avoided due to their high power efficiency. An HPSK (hybrid phase shift keying) modulation scheme has been proposed as the spreading technique for W-CDMA and cdma2000. HPSK is a complex spreading scheme that is designed to minimize the zero-crossing signal transition. However, there is a possibility that signal transition of HPSK go through zero. We propose a new modulation scheme for W-CDMA and cdma2000. The scheme is based on HPSK, but we modified the complex scrambling to avoid zero-crossing. We call it as modified HPSK (m-HPSK). It is shown that m-HPSK outperforms HPSK in the presence of non-linear amplification.
Classification of hyperspectral image using SCM methods for geobotanical analysis in the Brazilian savanna region

This work presents a spectral analysis of natural targets behavior of Cerrado using an AVIRIS sensor image (Airborne Visible/InfraRed Imaging Spectrometer). The sensor AVIRIS was brought to Brazil in 1995 in the SCAR-B (Smoke, Clouds and Radiation-Brazil) mission with the purpose of evaluating atmospheric effects. This activity was accomplished by NASA, INPE (National Institute of Space Research) and AEB (Brazilian Space Agency). During the SCAR-B mission, the AVIRIS sensor acquired image of Goias region, in August 16th 1995. The image used in this study presents geological structures that mark effects on the distribution of vegetation types. The aim of this work was to adapt and to test the employment of the spectral analysis in an AVIRIS hyperspectral image to differentiate vegetation patterns in order to compare with the geological structure. The atmospheric correction was performed using ATREM method (Atmosphere Removal Program) complemented by EFFORT (Empirical Flat Field Optical Reflectance Transformation) method. The endmembers were detected after the atmospheric correction according to the following steps: (a) spectral reduction by the Minimum Noise Fraction (MNF) transformation, (b) spatial reduction by the Pixel Purity Index (PPI) and (c) manual identification of the members using the N-dimensional visualization device. The employment of this technique allowed selecting a spectral series related to vegetation. The spectral analysis showed three main groups: (a) photosynthetic vegetation (PV), (b) non-photosynthetic vegetation (NPV) and (c) burned vegetation. The spectral classification was done using the SCM (Spectral Correlation Mapper) method. The SCM algorithm is a spectral classifier that presents advantages over Spectral Angle Mapper and Spectral Feature Fitting methods due to the ability to detect false positive results. The SCM was performed in the study image using the selected endmember means (photosynthetic active vegetation, NPV and burned areas). In classified images the most similar areas presented lighter colors, while the correlated areas were darkened. The color composition of the SCM classified images allowed the detection of differences in the vegetation distribution, enhancing structural and geomorphologic features of the study site.
Iterative multi-user decoding for asynchronous users

An iterative scheme for decoding asynchronous users on a multi-user adder channel is presented. In each iteration step, one user's a posteriori symbol probabilities are computed. The contribution of that user to the sum signal is partially cancelled such that the residual noise is minimized. Simulations show that two rate-2/3 users of a noiseless binary adder channel can be separated by a 4-state code.
Interuniversity'Master' in'Statistics'and' Operations'Research' UPC?UB'

Through Principal Component Analysis (PCA) we can reduce the dimensionality of the data to two or three dimensions, and display both cases and variables in a biplot. In order to approximate the original data values from the biplot we must use the inner products of the vectors corresponding to cases and variables, which can be confusing. Calibrated biplots solve this problem by locating graduation marks onto the variable vectors, such that we can read the approximated value projecting the cases points onto those calibrated vectors, the same way as in an ordinary scatterplot. But in biplots with many variables the abundance of calibration scales makes the plot difficult to read. Kohei Adachi (2010) presents an algorithm to implement for standardized data a technique called Unit-length Vector Analysis (UVA), whose aim is to constrain the variable vectors to be unit length, allowing to read the approximated data values projecting the cases points onto those constrained vectors, in a similar way as in calibrated biplots. This Master thesis develops in R code the algorithm proposed by Adachi, applies it to two data sets, and compares the Unit Vector biplots obtained with the corresponding Principal Component biplots in terms of goodness of fit. The results show that Adachi’s algorithm works well when there are no big differences in the contribution of the variables, and distributes the global fit in a way that some variables (those less well represented in PCA) gain contribution, and the others lose it. It forces the worst represented variables in PCA to gain representativity.
Mathematical Modeling of Scheduling Problems

Abstract The purpose of scheduling is to disaggregate the general production plan into time phased activities and determine a time table for activities which will optimize predetermined criteria. Minimizing flow times, keeping close conformance to deadlines, and efficient utilization of resources are the three broad classes of decision making criteria used in scheduling. In this paper, important criteria commonly used for evaluating scheduling decisions are identified. A scheduling cost model is developed for evaluating the cost of the schedule generated. Further, mathematical models are developed for generating optimal schedules in single machine, parallel machine, flow shop, flow shop with multiple processors, and job shop scheduling environments. The objective function in the mathematical models may consist of a single goal of minimizing the cost based upon a comprehensive scheduling cost model, or may consist of a set of goals composed of different scheduling criteria, both of which are modeled in thi.
Sensitivity of algebraic Riccati equations

The inherent conservatism in standard norm-based bounds for the sensitivity of the continuous-time algebraic Riccati equation is discussed and alternative sensitivity measures are introduced. These measures can be used to model a variety of situations where uncertainty in the data lead to an uncertain solution of the equation, and can be used to provide a more realistic evaluation of sensitivity than conventional bounds. The algorithmic sensitivity is discussed in this context. An efficient statistical approach for accurately estimating the sensitivity is described and examples of its different possible uses are given.
Pitch estimation using multiple independent time-frequency windows

A system for the detection of the pitch of musical sounds at a wide pitch range and in diverse conditions is presented. The system is built upon a pitch model that calculates independent pitch estimates in separate time-frequency windows and then combines them to yield a single estimate of the pitch. Both psychoacoustic and computational experiments were carried out to determine the optimal sizes of the elementary windows. The robustness of the system in wide-band additive noise and in the interference of another harmonic sound are demonstrated. An extension of the algorithm to the multi-pitch case is described, and simulation results for two-voice polyphonics are presented.
A Monte Carlo simulation approach to the capacitated multilocation transshipment problem

We consider a supply chain, which consists of N retailers and one supplier. The retailers may be coordinated through replenishment strategies and lateral transshipments, that is, movement of a product among the locations at the same echelon level. Transshipment quantities may be limited, however, due to the physical constraints of the transportation media or due to the reluctance of retailers to completely pool their stock with other retailers. We introduce a stochastic approximation algorithm to compute the order-up-to quantities using a sample-path-based optimization procedure. Given an order-up-to S policy, we determine an optimal transshipment policy, using an LP/network flow framework. Such a numerical approach allows us to study systems with arbitrary complexity.
The Trigger of Malicious Behaviors in Sandbox

To respond actively against malicious codes, analysts use automated investigation tools such as sandbox on the malwares. However, there has been the advent of malwares employing the various techniques to avoid the detection of the sandbox, which makes hard to identify the malicious behaviors of the samples codes. In this paper, we find the causes of evading the malicious codes. So, we propose the event generator to trigger malicious behaviors from the sample codes during virtual execution in the sandbox.
The Interpretation of Contrastive Stress in Spanish-Speaking Children

"The aim of this paper is to investigate Spanish children’s interpretation of pronouns across sentence boundaries. Referential dependencies established between pronouns and their antecedents are constrained by both syntactic, prosodic and discourse principles. In order to assign reference to a pronoun, a child needs to know these principles, but also needs the ability to apply them"