Miracle-dz/UniFAR-FTUs huggingface dataset:
AN ANALYSIS TO WEALTH DISTRIBUTION BASED ON SUGARSCAPE MODEL IN AN ARTIFICIAL SOCIETY

In this paper an artificial society is being assumed as a multi agents system. A sugarscape model consisting of a cellular landscape of resources is used to form an interaction among the agents of the population. In the model, agents find the resources to survive. They are supposed to move and search and because of this movement, an evolutionary social behavior will develope. From model analysis view point this behavior should be parameterized and also optimized. To analyze the said assumption, each agent should gather and store as much sugar as possible to create an asset for itself. Hence, From the simulation result, the population be categorized based on the asset. In the society, wealth may be allocated based on the asset, gathered by the agents. The percentage of population who will possesses some percentage of the wealth is specified. The simulation shows that in an artificial life, it is possible to use the sugarscape model to optimize the behavior of a society, and the parameters of the model are predictable as well.
Image Aquisition Order and Input Order of BOLD-fMRI Data for Slice Time Correction in fMRI Data Processing Tools

Using BOLD-EPI, one can obtain a whole brain coverage within a TR of a few seconds, but not every slice is acquired at exactly the same time. During an fMRI data preprocessing step, one can correct for the small time shifts using an algorithm called “slice time correction”. In this work, we present a solution to check how MR scanners acquire functional multislice images, we apply this method to extract the correct acquisition order of slices acquired with a Siemens 3T MR scanner and we derive the input for slice time correction to three fMRI data processing tools. Table 1 shows the resulting images of the eight different runs. Correct slice order input for SPM, FEAT (part of FSL) and BrainVoyager is listed in table 2.
ROLE OF TIE-POINTS DISTRIBUTION IN AERIAL PHOTOGRAPHY

Abstract. Automatic image matching algorithms, and especially feature-based methods, profoundly changed our understanding and requirements of tie points. The number of tie points has increased by orders of magnitude, yet the notions of accuracy and reliability of tie points remain equally important. The spatial distribution of tie points is less predictable, and is subject only to limited control. Feature-based methods also highlighted a conceptual division of the matching process into two separate stages – feature extraction and feature matching. In this paper we discuss whether spatial distribution requirements, such as Von Gruber positions, are still relevant to modern matching methods. We argue that forcing such patterns might no longer be required in the feature extraction stage. However, we claim spatial distribution is important in the feature matching stage. We will focus on terrains that are notorious for difficult matching, such as water bodies, with real data obtained by users of VisionMap’s A3 Edge camera and LightSpeed photogrammetric suite.
Sketch Engine for English Language Learning

There are many websites for language learners: wordreference.com, 1 Using English, 2 and many others. Some of them use corpus tools or corpus data such as Linguee, 3 Wordnik 4 and bab.la. 5 We introduce a novel free web service aimed at teachers and students of English which offers similar functions but is based on a specially prepared corpus suitable for language learners, using fully automated processing, offering the advantages that the corpus is very large – so can offer ample examples for even quite rare words and expressions. We call the service SkELL—Sketch Engine for Language Learning. 6
A tracking algorithm for both highly maneuvering and nonmaneuvering targets

Presents a modified version of the adaptive interacting multiple model (AIMM) algorithm, to enable tracking of both highly maneuvering and nonmaneuvering targets. The improvements include the addition of an adaptive sampling policy and a different gain calculation for the bias filter. The efficiency of the algorithm has been tested on the benchmark problem of the beam pointing control of a phased array radar. Comparisons with the algorithm utilizing fixed sampling rates are also given. The solutions obtained for the benchmark problem, which has many practical implementation aspects, compare very favourably with previous results.
Signature-based image identification

The use of digital images and video is growing on the Internet and on consumer devices. Digital images and video are easy to manipulate, but this ease of manipulation makes tampering with digital content possible. Examples of the misuse of digital content include violating copyrights of the content and tampering with important material such as contents of video surveillance. In this paper we present an algorithm that extracts a binary signature from an image. This approach can be used to search for possible copyright violations by finding images with signatures close to that of a given image. The experimental results show that the algorithm can be very effective in helping users to retrieve sets of almost identical images from large collections of images. The signature can also be used for tamper detection. We will show that the signatures we extract are immune to quantization errors that result from compression and decompression.
Improved MFCC feature extraction by PCA-optimized filter-bank for speech recognition

Although Mel-frequency cepstral coefficients (MFCC) have been proven to perform very well under most conditions, some limited efforts have been made in optimizing the shape of the filters in the filter-bank in the conventional MFCC approach. This paper presents a new feature extraction approach that designs the shapes of the filters in the filter-bank. In this new approach, the filter-bank coefficients are data-driven and obtained by applying principal component analysis (PCA) to the FFT spectrum of the training data. The experimental results show that this method is robust under noisy environment and is well additive with other noise-handling techniques.
Column Atmospheric Water Vapor Retrievals From Awborne Imaging Spectrometer Data

High spatial resolution column atmospheric water vapor amounts were derived from spectral data collected by the Airborne Visible Infrared Imaging Spectrometer (A VIruS). The quantitatiVe derivation is made by curve fitting observed spel:tra with calculated spectra in the 1.14 J.Lm and 0.94 J.LID water vapor band absorption regions with a non-linear least squares technique. The precision of the retrieved column water vapor amounts is approximately 5%. The derived column water vapor amounts are independent of the absolute surface reflectance. Curve fitting of spectra near 1 J.Lm from areas covered with vegetation indicates that both the amount of atmospheric water vapor and the moisture content of vegetation can be retrieved simultaneously. It now appears feasible to derive high spatial resolution column water vapor amounts over land areas from satellite altitude with the proposed High Resolution Imaging Spectrometer (HIRIS) and pos.sibly with the proposed Moderate Resolution Imaging Spectrometer (MODIS).
Signboard Text Translator: A Guide to Tourist

The travelers face troubles in understanding the signboards which are written in local language. The travelers can rely on smart phone for traveling purposes. Smart phones become most popular in recent years in terms of market value and the number of useful applications to the users. This work intends to build up a web application that can recognize the English content present on signboard pictures captured using a smart phone, translate the content from English to Telugu, and display the translated Telugu text back onto the screen of the phone. Experiments have been conducted on various signboard pictures and the outcomes demonstrate the viability of the proposed approach.
Estimating the parameters of neural mass models including time delay and nonlinearity using a particle filter: a preliminary study toward model‐based EEG analysis

Electroencephalogram (EEG) and local field potential (LFP) signals are measured for both experimental and clinical purposes which include sleep stage analyses, brain–computer interfaces, and disease diagnosis. EEG and LFP data analyses are typically based on models assuming that the measured data is generated from a biological system and estimate the model parameter values that most accurately reproduce the measured data. Thus, use of a biologically plausible model is important for a model‐based analysis. However, analyses using models that include time delay and nonlinearity have not been reported, even though they are biologically important for EEG generation mechanisms. In this study, we developed a parameter estimation method that uses a particle filter for models with time delay and nonlinearity, which was evaluated with simulations. Simulated EEG data were generated from neural mass models (NMMs). The NMM parameters were estimated from the generated data. Furthermore, parameters for modeling EEG features of patients with Alzheimer's disease were included in the NMM; the disease parameters were estimated from the simulated EEG data. We observed that NMM parameters, as well as the disease parameters, were accurately estimated from the simulated data. We conclude that the validity of our method for estimating parameters of NMMs including time delay and nonlinearity is confirmed for simulated EEG data, and these results show the possibility of using our method for model‐based analysis with real EEG data. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Distributed Load Balancing Model for Grid Computing

Most of the existing load balancing strategies were interested in distributed systems which were supposed to have homogeneous resources interconnected with homogeneous and fast networks. For Grid computing, these assumptions are not realistic because of heterogeneity, scalability and dynamicity characteristics. For these environments the load balancing problem is then a new challenge presently for which many research projects are under way. In this perspective, our contributions through this paper are two folds. First, we propose a distributed load balancing model which can represent any Grid topology into a forest structure. After that, we develop on this model, a load balancing strategy at two levels; its principal objectives : the reduction of average response time of tasks and their transferring cost. The proposed strategy is naturally distributed with a local decision, which allows the possibility of avoiding use of wide area communication network.
Learning Dynamic Network Models for Complex Social Systems

Human societies are inherently complex and highly dynamic, resulting in rapidly changing social networks, containing multiple types of dyadic interactions. Analyzing these time-varying multiplex networks with approaches developed for static, single layer networks often produces poor results. To address this problem, our approach is to explicitly learn the dynamics of these complex networks. This dissertation focuses on five problems: 1) learning link formation rates; 2) predicting changes in community membership; 3) using time series to predict changes in network structure; 4) modeling coevolution patterns across network layers and 5) extracting information from negative layers of a multiplex network. To study these problems, we created a rich dataset extracted from observing social interactions in the massively multiplayer online game Travian. Most online social media platforms are optimized to support a limited range of social interactions, primarily focusing on communication and information sharing. In contrast, relations in massively-multiplayer online games (MMOGs) are often formed during the course of gameplay and evolve as the game progresses. To analyze the players’ behavior, we constructed multiplex networks with link types for raid, communication, and trading. The contributions of this dissertation include 1) extensive experiments on the dynamics of networks formed from diverse social processes; 2) new game theoretic models for community detection in dynamic networks; 3) supervised and unsupervised methods for link prediction in multiplex coevolving networks for both positive and negative links. We demonstrate that our holistic approach for modeling network dynamics in coevolving, multiplex networks outperforms factored methods that separately consider temporal and cross-layer patterns.
Classification of emotional speech using spectral pattern features

Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition are proposed. These features extracted from the spectrogram of speech signal using image processing techniques. For this purpose, details in the spectrogram image are firstly highlighted using histogram equalization technique. Then, directional filters are applied to decompose the image into 6 directional components. Finally, binary masking approach is employed to extract SPs from sub-banded images. The proposed HEs are also extracted by implementing the band pass filters on the spectrogram image. The extracted features are reduced in dimensions using a filtering feature selection algorithm based on fisher discriminant ratio. The classification accuracy of the proposed SER system has been evaluated using the 10-fold cross-validation technique on the Berlin database. The average recognition rate of 88.37% and 85.04% were achieved for females and males, respectively. By considering the total number of males and females samples, the overall recognition rate of 86.91% was obtained.
Investigating Factors Affecting Group Processes in Virtual Learning Environments

With the widespread popularity of distance learning, there is a need to investigate elements of online courses that continue to pose significant challenges for educators. One of the challenges relates to creating and managing group projects. This study investigated business students’ perceptions of group work in online classes. The constructs of learning and social interaction, process satisfaction, product satisfaction, and use of technology in the virtual learning environment were investigated. The use of social media networks by group participants was also examined. Recommendations are provided for business educators looking to develop or enhance teamwork in virtual learning environments.
Microsimulations: Bridging theory and practice in the composition practicum

Many new teaching assistants have concerns about keeping authority in the classroom. These concerns make it more difficult for the teachers of the composition practicum to focus discussions on other important pedagogical issues. This study found that microsimulations were an effective way of allowing graduate students to respond to challenges to their authority in a low-stakes environment. The study also found that microsimulations helped future teachers bridge composition theory with practice and introduced them to a form of adventuresome teaching.
Evaluating CUES: Some Problems and Issues in Experimental CALL Research

The effectiveness of a computer program, CUES, designed to improve adult ESL students' essays was tested both formally and informally. The formal measures carried out in both the experin7eiital and the control groups included: 1) holistic ratings of both groups' pre- and post- intervention essays, and 2) a comparison of pre- and post-intervention thinking-aloud protocol analyses. The informal measures included a questionnaire administered to the students and critical comments about the program garnered from the students' responses in a 500-ivord essay entitled, "Assess the program CUES. " This research provides an opportunity for the author to examine critically the research methods used, and to conclude that both formal and informal evaluation measures provide a balanced assessment of CALL software.
Automatic generation of fuzzy rulebase for robot arm posture selection

When performing a manipulation task, through experience we place our arm in a posture which is best suited for performing that task. Although indices for quantifying the optimality of robot arm postures with respect to task requirements have been formulated in the past, they have not been used in practice for posture selection. The primary reason for this lack of use is that finding the optimal posture according to these indices is a highly nonlinear, high dimensional optimization problem. There are many local minima that are difficult to avoid unless the initial posture (i.e., initial conditions) is already near the true optimum; and there are no guidelines for choosing initial postures. Using a cluster estimation method, we have automatically generated a fuzzy rulebase for selecting the initial posture for a redundant robot arm. From 10,000 training data relating arm posture to arm performance characteristics, 138 rules were generated. In essence, the rulebase of 138 protypical postures; each posture has a fuzzy range of performance characteristics. Given a set of desired performance characteristics (e.g., move fast in the x direction, exert force in y direction) as the input to the rulebase, the degree of fulfillment of each rule is the degree to which the posture's performance characteristics matches the task requirements. The posture with the highest degree of match is selected as the initial posture. <<ETX>>
Low-power CMOS/BiCMOS drivers and receivers for on-chip interconnects

Novel low-voltage swing CMOS and BiCMOS driver/receiver circuits for low-power VLSI applications are proposed. Interconnect wire drivers with low output signal swing are employed. Special receivers provide single and double level conversion while minimizing the total driver/receiver transmission delay. These level converters have no DC power dissipation. At 3.3 V power supply voltage, the proposed circuits consume less power without delay penalty. The power saving is observed to be as high as 30%. At lower supplies further power and delay improvements are observed. >
Automated Local Newsletter Generator

This project aims to automatically generate local newsletter by applying topic classification and geographic reference. Support Vector Machine (SVM) is employed for the topic classification task. To retrieve only user entered location name related documents, we apply geographic reference to validate location name, enrich query with alias names and solve place name ambiguity problem. The dataset of 20-newsgroup that is collected from 20 different news sites is used in this project. For SVM classifier construction, we use LibSVM and for the geographic reference application, we employ Gazetteer, WordNet and Named Entity Tagger. The text preprocessing process includes stemming, stop words removal, too short/long or frequent/infrequent words deletion. The TF-IDF weight calculation is applied to convert text into vectors and scripts provided by LibSVM are applied to search optimal parameters for classifier construction. The Gazetteer is used to check whether the user entered text is a valid location name and the WordNet service is applied to collect all possible alias names for a place. Named Entity Tagger is employed to extract only location named entities for document filtering. With the collection of alias names collection and location named entities, we search documents to retrieve only local related documents as expected. After implementing this automatic local newsletter generator, we analyse experimental results in several ways: Effectiveness of text preprocessing, optimal number of features and optimal parameters for classifiers, location name enrichment, and location named entity identification. The best feature number and optimal parameters are chosen by experiments for our final classifier. The experimental results show that our project is able to successfully retrieve local information from numerous documents and our solutions for topic classification and geographic reference are significant and effective. Lihua Zhao, Machine Learning and Data Mining I Automated Local Newsletter Generator
Corrective feedback in the chatroom: An experimental study

This paper replicates in a synchronous learning environment a study that looked at the effectiveness of providing two types of corrective feedback to students in the classroom. Elementary learners of L2 English (N = 31) completed two communicative tasks, during which time they received either recasts (implicit feedback) or metalinguistic information (explicit feedback) in response to any utterance that contained an error in the regular past tense. A third group served as a control. Learning was measured by timed and untimed grammaticality judgement tests. Results, which contrast with that of the original study, show no statistically significant gains in response to either type of feedback. An examination of chatscripts suggests some possible reasons as to why learning, as measured on the tests used, did not take place in a computer-mediated communicative context.
Human Action Recognition in Videos Using Distance Image Volumes and Sparse Coding

Human action recognition in video has been a difficult challenge due to the diversity of different action of different actor. In this paper, distance image volume is proposed to describe actions in videos, which is combined with sparse coding method for human action recognition. Different from other approaches that use both appearance and motion features or temporal-spatial interest points for better recognition performance, our approach only uses the distance image volumes that can present the appearance and motion information of human action but with low computation. For action recognition, we propose the use of sparse coding (SC), in which dictionary is represented by distance image volumes of training action videos. Each test video is then constructed by a linear combination of the basis vectors of the dictionary. The residuals between the test video and each action class are computed for determining the identity. The experiments are conducted on publicly available data set Weizmann. The results demonstrate that distance image volume feature combined with sparse coding method helps in achieving improved performance.
Optimized Grey Model Based on Cuckoo Search Algorithm and Its Prediction Application

The prediction is complex structure and the grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. GM(1, 1) model is one of the frequently used basic model of grey prediction with simple structure. Based on the grey prediction modeling theory and with an analysis of the shortages of GM(1, 1) model, an adaptive multi-variable optimized grey model based on cuckoo search algorithm is proposed, which reconstructs whitening background values and initial conditions in view of the relationship among many related reduction feature information. Several experiments are taken for prediction and analysis, and the results show that the new model has high precision and practicability.
Efficient detection of dangling pointer error for C/C++ programs

Dangling pointer error is pervasive in C/C++ programs and it is very hard to detect. This paper introduces an efficient detector to detect dangling pointer error in C/C++ programs. By selectively leave some memory accesses unmonitored, our method could reduce the memory monitoring overhead and thus achieves better performance over previous methods. Experiments show that our method could achieve an average speed up of 9% over previous compiler instrumentation based method and more than 50% over previous page protection based method.