Miracle-dz/UniFAR-FTUs huggingface dataset:
Session 4: Experimentation in the Process Framework

What does experimentation in the process framework mean? After all, processes enacted as parts or all of a software development are one data point right? A s you can observe f rom these two introductory sentences, our models and our language f o r discussing empirical work in software engineering, and in particular, a process framework are not well developed. This session was a start at trying to discuss and provide some of the foundations fo r models and language of experimental work when discussing processes involving more than one or two people. The workshop session consisted of two parts. The first part was a general background section on areas relevant t o experiments involving many people. W e described some of the properties of good experiments and a research bibliography was made available to start the work of building a hybrid empirical research paradigm somewhere between the physical and social sciences. The second part was a discussion of some of the key issues of concern to the participants at the workshop. These issues included the role of academia in work involving large non-repeatable developments and the role of tailorability desired/required by the project manager in large software developments. Larry began by presenting his goals for the session, his assumptions, some terminology, some of his own personal ”pearls”, some good examples of experimental work, and his criteria for a good experiment. The goals for the session were to: chart the path by identifying the big questions for empirical study; establish common terminology; discuss the credibility of erriplrical work; and share our pearls, lessons, and experience with experimentation. Larry presented several points he considered assumptions on which to base the discussion. First, single person studies are well established. Second, and ironically, team and organizational studies have been poorly framed, even though this is where softwxe process research should have the most impact. Finally, it 0-8186-6770-2/95 $04.00
A Hybrid Data Association Approach for SLAM in Dynamic Environments

Data association is critical for Simultaneous Localization and Mapping (SLAM). In a real environment, dynamic obstacles will lead to false data associations which compromise SLAM results. This paper presents a simple and effective data association method for SLAM in dynamic environments. A hybrid approach of data association based on local maps by combining ICNN and JCBB algorithms is used initially. Secondly, we set a judging condition of outlier features in association assumptions and then the static and dynamic features are detected according to spatial and temporal difference. Finally, association assumptions are updated by filtering out the dynamic features. Simulations and experimental results show that this method is feasible.
Performance evaluation of transform based feature extraction methods for identity authentication system using fingerprint matching

Fingerprint verification is an important biometric technique for personal identification. This paper presents a performance evaluation of different fingerprint feature extraction methods. Fingerprint matching scheme based on transform features, like DCT (Discrete Cosine Transform), FFT (Fast Fourier Transform) and DWT (Discrete wavelet transform), have been presented and compared. In the fingerprint recognition application utilizing more information other than minutiae is much helpful. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The transform coefficients are used to obtain the feature vector in terms of standard deviation and energy. Matching is performed using fast Euclidean distance between two feature vectors. The algorithms have been tested on database available from University of Bologna. Our comparison shows that DCT and FFT yields better GAR (Genuine acceptance rate) at low FAR (False acceptance rate) with reduced computational complexity over existing DWT and Gabor based algorithms. Because of reduced computational complexity these algorithms can be easily implemented as an embedded automatic fingerprint identification system (AFIS).
Equity and Commodity Market Co-Movements in Thailand

This paper studies the dynamic linkage between oil price, gold price and Thailand stock market in the long run as well as in the short run. By employing Time series analysis using monthly data from for the period of August 1999 to August 2013, the study applies co-integration technique, Granger causality test, and Impulse Response Function (IRF) and Variance Decomposition (VDC) analysis. The findings reveal that oil price changes have asymmetric effects on Thailand stock market while a positive relationship has been observed between the gold price and the stock return in the same market. Finally, few implications and applications of the study have been suggested in detail.
R3ST for Requirements Recovery of Legacy Runtime Code

 Abstract—In reality, we often find that proven and workable software, exist without source code. Identifying end-to-end interaction within existing legacy runtime code is a promising way for requirements recovery. Those identified interaction forms a set of use cases. Based on use case modeling concept, ontology is created for each finding interaction in order to check for completeness and consistency. OWL serves as platform for ontology development. That ontology reflects the recovered requirements. The procedures to identify the end-to-end interaction has been formulated and tested for university information systems. R3ST, tighly couple to Prote ge , has been developed with the objective to assist and automate those procedures.
Fuzzy Weighted Gaussian Mixture Model for Feature Reduction

reduction is one kind of pattern recognition and decision making technique, which can be achieved by using Fuzzy Weighted Gaussian Mixture Model (FWGMM) based on the Gaussian Mixture Model. This model helps to find relevant features by using Fuzzy ordered weighted average, which leads to determine the similarity of the density mixture. The salient feature of this approach is to find the relevant features simultaneously by employing fuzzy weighted approach. By applying Ordered Weighted Average (OWA), the feature weights are calculated and they are ordered using the membership values (oring criterion). Hence the feature weights are used as a regulator to determine the relevant features in feature reduction process. Maximum Ordered Weighted Average Likelihood (MOWAL) Framework adopts the Fuzzy Weighted - Gaussian Mixture Model (FW-GMM) for finding the component, which helps to discriminate the relevance of the features and improve the accuracy of density mixture.
Cartesian Adaptive Mesh Refinement for Rotorcraft Wake Resolution

We explore the use of high-order algorithms within a block-structured Cartesian adaptive mesh refinement (AMR) solver to capture shed wakes from rotary-wing vehicles. The adaptive Cartesian solver is used as an off-body solver, in conjunction with a viscous near-body unstructured solver to capture wall boundary layer effects. The off-body solver automatically adapts to resolve time-dependent flow features, refining and coarsening throughout the unsteady simulation, with mesh refinement targeting regions of high vorticity. Accuracy and performance of the adaptive solver are tested for resolving the tip vortices from a wing at angle of attack and the wake of a quarter-scale V-22 helicopter rotor in hover conditions.
Intraday End-of-Day Volume Prediction*

A model that predicts the total end of day volume and updates the prediction as new intraday information is observed is proposed. This model is a time varying coe cients model which is estimated using the framework of functional data. Semiparametric constraints such as monotonicity of the time varying coe cients can be imposed in the estimation. Results that allow us to derive con dence bands under a variety of scenarios including functions that lie at the boundary of the constrained set are given. In the empirical application we consider the end of day volume prediction of major Forex futures traded on the Chicago mercantile Exchange and show the bene ts of the proposed methodology.
The Effect of Generic Headphone Compensation on Binaural Renderings

Binaural rendering allows us to reproduce auditory scenes through headphones while preserving spatial cues. The best results are achieved if the headphone effect is compensated with an individualized filter, which depends on the headphone transfer function, ear morphology and fitting. However, due to the high complexity of remeasuring a new filter every time the user repositions the headphone, generic compensation may be of interest. In this study, the effects of generic headphone equalization in binaural rendering are evaluated objectively and subjectively, with respect to unequalized and individually-equalized cases. Results show that generic headphone equalization yields perceptual benefits similar to individual equalization for non-individual binaural renderings, and it increases overall quality, reduces coloration, and improves distance perception compared to unequalized renderings.
Study of Multiple View Layout Strategies in Visualisation

A BSTRACT Multiple views is a popular strategy in information visualisation, but for many years researchers have asked questions such as “how many views and what layout strategies do people use? ” Answering these questions would help developers create suitable multiple-view systems, but to date there has been little research into these questions. In this short paper, we present initial results of a larger ongoing study looking at how multiple-views are used. For this study, we built a database of images containing screenshots of visualisation tools from articles presented at IEEE VIS from 2012 to 2017. We select suitable images across TVCG journal, conference, posters and workshop papers. We closely evaluate the layout of 340 images of multiple-view systems and consider the layout topology of each image. Our results show that in the past six years, developers use on average (just over) four views.
Joint source-channel coding in the presence of feedback

The effect of the presence of a feedback channel on the transmission of information was first considered by Shannon who showed that the capacity of a memory-less channel is not increased by feedback. Later it was shown that the information on a feedback channel can be used to improve the performance of channel coding. Here we show that by employing the feedback information in quantizer design for a noisy channel, one can achieve lower overall distortion compared to the case where feedback is not available or the quantizer is not matched to the channel. We consider an adaptive and a non-adaptive quantization scheme which show that especially for channels with high error rate, feedback information can lead to lower distortion compared to the case where no feedback information is available. <<ETX>>
An Automatic Heart Wall Contour Extraction Method on MR Images using the Active Contour Model

In this paper, we propose a new method of extrating heart wall contours using the Active Contour Model (snakes). We use an adaptive contrast enhancing method, which made it possible to extract both inner and outer contours of the left ventricule of the heart. Experimental results showed the efficiency of this method.
BI-BAND BOW -TIE ANTENNAS ARRAY DESIGN USING A SIMPLE EQUIVALENT TRANSMISSION LINE MODEL

In this paper we propose a simple equivalent and accurate transmission line model for bi-band bow-tie antennas array design over a band of frequencies for satellite communications. This model uses the resistance of a square element that appears at the edges of the antenna (radiating slots). To test this model, two antennas array were simulated and results were compared with those obtained by a rigorous method (moment’s method) of the simulator ADS/Momentum. Using this transmission line approach the resonant frequency, return loss, VSWR, reflected phase, input impedance can be determined simultaneously. The results confirm the validity of the proposed model.
Low-Power Write-Circuit with Status-Detection for STT-MRAM

We report a STT-MRAM write-scheme, in which the length of the write-pulse is determined dynamically by sensing the status of MTJ cells. The proposed scheme can reduce the power consumption by eliminating unnecessary writing current after the switching has occurred. We also propose a reference cell design, which is optimized for the use in writecircuits. The performance of the proposed circuit was verified by SPICE level simulations of the circuit implemented in a 0.13 μm CMOS process.
Automatic Acquisition of Subcategorization Frames From Untagged Text

This paper describes an implemented program that takes a raw, untagged text corpus as its only input (no open-class dictionary) and generates a partial list of verbs occurring in the text and the subcategorization frames (SFs) in which they occur. Verbs are detected by a novel technique based on the Case Filter of Rouvret and Vergnaud (1980). The completeness of the output list increases monotonically with the total number of occurrences of each verb in the corpus. False positive rates are one to three percent of observations. Five SFs are currently detected and more are planned. Ultimately, I expect to provide a large SF dictionary to the NLP community and to train dictionaries for specific corpora.
Facial expression recognition based on local facial regions

In facial expression recognition, the extraction of expression features is most important. It is a complex problem that how to extract steady and effective expression features from facial images which contain much disturbance. lt is the key to improve recognition rate, too. In this paper, four local regions such as mouth, nose, eye (including the eyebrows) and glabella that are extracted. Our experiments are performed on the database of CED-WYU (1.0). And the experiment results explain our understanding about facial expression recognition based on local facial regions.
Buzz-and-Pipeline Dynamics: Towards a Knowledge-Based Multiplier Model of Clusters

This article critically reviews the idea that regional prosperity and growth are heavily dependent on regional industry networks. In contrast with this view, a cluster approach is presented which emphasises both the need for close local networks and strong extralocal or global linkages. The approach argues that local interaction or ‘buzz’ and interaction through translocal ‘pipelines’ create a dynamic process of learning, knowledge production and innovation that is central to understand a cluster's success. Based on a reflexive relationship between local and non-local knowledge flows, this approach is interpreted as a knowledge-based extension of regional multiplier models that have been intensively discussed in regional economics since the 1960s. The buzz-and-pipeline conception suggested here aims to overcome problems of these models by providing a microscale explanation of regional growth processes.
A case for resource efficient prefetching in multicores

Hardware prefetching has proven very effective for hiding memory latency and can speed up some applications by more than 40%. However, this speedup comes at the cost of often prefetching a significant volume of useless data which wastes shared last level cache space and off-chip bandwidth. This directly impacts the performance of co-scheduled applications which compete for shared resources in multicores. This paper explores how a resource-efficient prefetching scheme can benefit performance by conserving shared resources in multicores. We present a framework that uses fast cache modeling to accurately identify memory instructions that benefit most from prefetching. The framework inserts software prefetches in the application only when they benefit performance, and employs cache bypassing whenever possible. These properties help reduce off-chip bandwidth consumption and last-level cache pollution. While single-thread performance remains comparable to hardware prefetching, the full advantage of the scheme is realized when several cores are used and demand for shared resources grows.
Synchronous architectures for high precision processing of SAR data: experiments and results

Considers a two-dimensional synthetic aperture radar processing approach and compares the precision processing performances on three different synchronous general purpose computers: a RISC processor, a vector processor and a SIMD architecture with thousands of elementary processors. Results of computation provide an idea of the cost/benefit performance of the three systems for SAR applications. <<ETX>>
Improving Data Availability Using Combined Replication Strategy in Cloud Environment

: As grow as the data-intensive applications in cloud computing day after day, data popularity in this environment becomes critical and important. Hence to improve data availability and efficient accesses to popular data, replication algorithms are now widely used in distributed systems. However, most of them only replicate the static number of replicas on some requested chosen sites and it is obviously not enough for more reasonable performance. In addition, the failure of request is one of the most common issue within the data centers. To compensate these problems, we, propose a new data replication strategy to provide cost-effective availability, minimize the response time of applications and make load balancing for cloud storage. The proposed replication strategy has three different steps which are the identification of data file to replicate, placing new replicas, and replacing replicas. In the first step, it finds the most requested files for replication. In the second step, it selects the best site by consideration of the frequency of requests for replica, the last time the replica was requested, failure probability, centrality factor and storage usage) for storing new replica to reduce access time. In the third step, the replacement decision is made in order to provide better resource usage. The proposed strategy can ascertain the importance of valuable replicas based on the number of accesses in future, the availability of the file, the last time the replica was requested, and size of replica. Our proposed algorithm evaluated by CloudSim simulator and results confirmed the better performance of hybrid replication strategy in terms of mean response time, effective network usages, replication frequency, degree of imbalance, and number of communications.
Energy Aware Optimal Routing for Wireless Sensor Networks

Wireless Sensor Network (WSN) is a collection of autonomous devices with computational, sensing, and wireless communication capabilities. The sensor nodes are low cost, multi-functional devices that are densely deployed either inside the phenomenon or very close to it and are often powered by independent power sources. The lifetime of such sensor nodes show a strong dependence on lifetime of the power source used. So, conservation of power and hence increase the lifetime becomes an important issue in WSNs. Routing in Wireless Sensor Networks, which consumes a considerable amount of energy, offers ample scope in increasing the life time of WSNs. To address this problem an Energy Aware Optimal Routing (EAOR) algorithm, which determines the path that consumes minimum power between the source node and the destination node, is proposed here. The proposed EAOR algorithm performs better in terms of conservation of power compared to the shortest path routing algorithm. This EAOR algorithm is also validated through extensive simulation in various scenarios, like increasing the number of sensor nodes and gateway nodes. The results show that EAOR algorithm performs well for all the scenarios.
Equações discretas no ensino médio: modelos de dinâmicas populacionais

The discrete equations provide the basic mathematical tools for the correct modeling of population dynamics to be taken as a discrete event times. In this paper, we present the theory of discrete equations and some applications in Chapter 01, with emphasis on models of population dynamics model such as Malthus and Verhulst along with examples that give evidence such models in the modeling of real populations. In chapter 02 we present two proposals teaching: the first is an analysis of graphs relating age and height and the second, an investigation of the population dynamics of the city of Limeira through Malthus and Verhulst models and presents a comparative analysis of models as fit to the data as presented and are able to follow the official projections. The graphs were created in free software GeoGebra.
Serial order in short-term memory

How do we maintain a novel sequence of items in the correct order? For example, how do we remember the car number plate at the scene of a crime? Or how do we remember an unfamiliar telephone number during the few seconds between putting down the telephone directory and picking up the telephone? This immediate serial recall or ‘memory-span’ task has fascinated psychologists for decades; it has remained the dominant empirical tool behind contemporary theories of short-term memory, such as Alan Baddeley’s working-memory theory (Baddeley, 1986). However, like many questions in cognitive psychology, the apparent ease with which we perform such a simple task (providing the telephone number is not too long! ) masks a rich and complex host of issues.
Clustering and alignment of physiological time series

Microprocessors which can be used to collect data are embedded in more and more everyday objects. This phenomenon is known as pervasive computing and results in large amounts of data. These data can contain valuable information, but extracting this information is not a trivial task. For example, a data set containing the values of a person’s heart rate can be collected using a smart watch; however, the size of such a data set will rapidly grow beyond the point where manual inspection is possible. The aim of this master project is to determine a description of a characteristic pattern which reoccurs in a given data set. This description can be either interpersonal or intrapersonal; one may think of the development of the heart rate of athletes during a race, the recognition of words based on speech patterns, or the development of a person’s skin temperature during sleep. A major problem when trying to achieve this goal is that it is not necessarily the case that all data sets contain similar patterns. Therefore, clusters containing data sets with similar patterns must be determined, followed by the construction of a data set which describes all the data sets in the cluster; such a data set is called an alignment. Several alignment algorithms are evaluated in this master project, after which the one most suitable is extended in such a way that it generates an alignment based on multiple data sets. The algorithm also visualizes the reliability of the alignment; the more data sets containing a similar part in the alignment, that more reliable is that part. Data sets often contain outliers, which have a negative effect on the quality of the determination of clusters and alignments. Therefore, an outlier removal algorithm is developed based on an existing one; this algorithm is compared with existing outlier removal algorithms and performs better on data sets with varying characteristics. The main advantage of this algorithm is that it is fully automated, which means that no knowledge of the frequency of outliers occurring in the data set is required. To validate these developed algorithms on a real world data set, a case study is conducted on a data set consisting of the skin temperature, skin conductance, and heart rate data of one person. The aim of the case study is to determine the characteristic patterns of these physiological features during sleep. The nights are clustered on the basis of similarity of the data sets of the feature under inspection and an alignment for each cluster is generated. Both the clustering algorithm and the alignment algorithm provide desirable results.