Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning challenges faced by novice programming students studying high level and low feedback concepts

This paper describes an investigation into the nature of the academic problems that face novice programming students. These learners are required to demonstrate competencies in high-level abstract principles of programming and logic, such as program design and OOP principles, which are conceptually difficult. During the programming task learners receive relatively high levels of feedback on low level issues, such as syntax rules, but tend to receive low levels of feedback on conceptually more difficult issues. This problem tends to be exacerbated by the trend of learners to study independently, outside the classroom or in online modes, which further reduces the options available for quality feedback on high-level issues. This paper analyses the results of a survey given to students enrolled in an introductory programming unit across three campuses at Monash University in 2007. The survey focused on student perceptions of the relative difficulty in understanding and implementing both low level-programming concepts, such as syntax and variables, and high-level concepts, such as OOP principles and efficient program design. An analysis of the approximately 150 responses has indicated that a significant percentage of students experienced difficulties in high-level concepts. Also while many students may indicate an understanding of the principles of many high level concepts more students reported experiencing difficulty in implementing such concepts. This indicates that many students may achieve a level of understanding allowing near transfer of domain knowledge but fail to reach a level of understanding that enables far transfer.
Analysis of large data sets using

In this paper, the attention is directed towards application of Proper Orthogonal Decomposition (POD), with special focus on analysis of large data sets. Using modern tools it is common that simulations or measurements of flows result in very large amounts of data. Today, the principal difficulty isto be able to manage the large data set to post-process it and thereby compute the quantities of interest. To study the dynamics of the large scale structures it is common to apply POD. Here, analysis of three flow cases are presented. The common denominator among the flows is that the amount of data to process is vast. The three cases investigated are: direct numerical simulation (DNS) of stably stratified shear flow, DNS of a channel, and large eddy simulation (LES) of a compressible boundary layer.
Instructional Applications of Information Architecture

It is commonly stated that we live in an age of “information overload. ” Increasingly advanced communication tools, including the Internet and digital telecommunication technologies, allow us to transmit more and more information at faster and faster rates. A discipline— known variously as information design or information architecture— has evolved to meet the challenge of using information to provide meaningful communication. The discipline embraces such diverse fields as business administration, computer science, cognitive psychology, graphic and typographic design, and technical communication. Information architecture is the structuring of data to meet the informational and management requirements of an organization or group of people. For example, in their work on designing web sites, Rosenfeld and Morville (1998) state that the information architect is responsible for clarifying the mission and vision of a web site; for determining the site’s content and functionality; for specifying how users will find information by defining the site’s organization, navigation, labeling, and searching systems; and for mapping how the site will change and grow over time (Rosenfeld & Morville, 1998, p. 11). This paper briefly examines some theoretical underpinnings of information architecture, considers the application of the principles of information architecture to instructional theory and practice, and describes ongoing research to evaluate the efficacy of such an application.
RAP: a ring array processor for multilayer perceptron applications

A ring array processor (RAP) designed for fast simulation of artificial neural network algorithms is described. The RAP is a multiprocessor system which is particularly targeted in the training of feedforward networks for the recognition of continuous speech. The overall system consists of several four-processor boards serving together as an array processor for a 68020-based host running a real-time operating system. The prototype design includes 64 Mbytes of dynamic memory (expandable to 256) and 4 Mbytes of fast static RAM distributed between 16 processors on four boards. Theoretical peak performance is 512 MFLOPS, and simulations have indicated a sustained throughput of roughly half of this for algorithms of current interest, including communication overhead of roughly 10-30%. Initial tests with the new hardware have confirmed the major assumptions of the estimates from the simulations. <<ETX>>
Autonomous Machine Learning (ALMA) : generating rules from data streams

In this paper the well known problem of automatically generating tractable models (for example, but not limited to fuzzy rules) from data by learning will be given a new impetus by introduction of the concept of Autonomous Learning MAchines (ALMA). This concept increases the level of automation and autonomy of the process of model identification and design by reducing the need for human intervention in parts of the problem where this is traditionally done manually and off-line such as model structure identification. The proposed concept is generic and is not limited to fuzzy rule based (FRB) systems or neuro-fuzzy (NF) systems, but is also applicable to hidden Markov models (HMM), decision trees between others. For the specific case of FRB and NF models which are considered in this paper, the membership functions (MF) are automatically generated from (and represent) the true data distribution using kernels and data clouds and recursively estimated relative data density. In this paper we propose an original method for evolving clouds from data based on the well known mean-shift approach and we propose an evolving version of it which we call evolving local means (ELM). The proposed ALMA can be used as a basis of software algorithms and agents and hardware devices in a wide range of problems in industry, defense, security, space exploration, robotics, human behavior analysis, assisted living, etc.
Regression Based Gaze Estimation with Natural Head Movement

This thesis presents a non-contact, video-based gaze tracking system using novel eye detection and gaze estimation techniques. The objective of the work is to develop a real-time gaze tracking system that is capable of estimating the gaze accurately under natural head movement. The system contains both hardware and software components. The hardware of the system is responsible for illuminating the scene and capturing facial images for further computer analysis, while the software implements the core technique of gaze tracking which consists of two main modules, i.e., eye detection subsystem and gaze estimation subsystem. The proposed gaze tracking technique uses image plane features, namely, the inter-pupil vector (IPV) and the image center-inter pupil center vector (IC-IPCV) to improve gaze estimation precision under natural head movement. A support vector regression (SVR) based estimation method using image plane features along with traditional pupil center-cornea reflection (PC-CR) vector is also proposed to estimate the gaze. The designed gaze tracking system can work in real-time and achieve an overall estimation accuracy of 0.84o with still head and 2.26o under natural head movement. By using the SVR method for off-line processing, the estimation accuracy with head movement can be improved to 1.12o while providing a tolerance of 10cm×8cm×5cm head movement.
CELLULAR AUTOMATA WITH CHAOTIC RULE FORMATION AND ITS CHARACTERISTICS

The fractal procedure known as cellular automata are applied to the different field.Cellular Automata are not only of theoretical interest. One of the main topics in CA research is their usage as models of phenomena which can be observed in real life, the practical applications of Cellular Automata can be seen in multiple fields of science and social science. They range from computer science, technology, physics, biology, and math, to economics, psychology, philosophy, and even art. Cellular Automata are discrete dynamical systems, of simple construction but complex and varied behavior.Despite of the simplicity of their construction; Cellular Automata are capable of very complicated behavior with very little specification. The present paper introducesa cellular automata and their applicationwith some rules. It is very useful for future references.
Eye tracking recycle labels on packaging: Are attitudes and behaviors a predictor of viewing them?

In the United States, the use of the chasing arrows is the most recognizable label for consumers to interpret the recyclability of packaging and other materials. Yet, the use of the label can be used interchangeably for different purposes, which may lead to misinterpretation of the label. Recently, the Sustainable Packaging Coalition (SPC) revamped the recycle label to provide more information, avoid confusion, and misinterpretation. Recycle rates have only increased seven percent in the past decade. Furthermore, there is a lack of research about the utility of recycle labels in efforts to increase recycling rates. The over-arching goals of this research was twofold. First, it was to see if attitudes and behaviors predict seeing the recycle label. Secondly, it was to evaluate whether the location of the recycle label affects consumers viewing it. Our study used eye tracking as a methodology to observe viewing the SPC's recycle label on four cereal packages. Two of the packages were simulated natural clay coated paperboard and two were white clay coated paperboard. The locations were alternated on the packaging. We administered two surveys; one attitudinal and the other behavioral after the participants viewed the packages (N=61). The surveys were analyzed using factor and discriminate analysis to derive whether attitudes and behaviors are predictors of viewing the recycle label. Eye tracking provided data to find what kinds of labels are glanced at most often on packaging. Based on our sample, our findings indicate that attitudes and behaviors are not good predictors of viewing the recycle label. However, attitudes should be considered for iii placing the recycle label on natural packaging. Eye tracking provided evidence that the recycle label does not attract as many glances in comparison to other labels. Also, participants saw the recycle label more frequently on the back of the package than the bottom of the package. iv DEDICATION This thesis is dedicated to my wonderful and most loving father, Ken McKelvey. You have always encouraged me to challenge myself and take the roads less traveled, because if we only do things we are familiar with, we might miss out on what we are made for. To get me through this journey, you said to pray and listen to God, and then wait patiently on the answers. When you get the answer, obey no matter how difficult the circumstances may be. On this endeavor, you have always been there to offer me …
Recognizing Client Identity in Facebook Utilizing Their Personality Information

— The facilitators of human interactions, social networks have become an interesting target of research, providing rich information for studying and modeling user’s behavior. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in our current research efforts. This paper explores the feasibility of modeling user personality based on a proposed set of features extracted from the Facebook data. The encouraging results of our study, exploring the suitability and performance of several classification techniques, will also be presented. Gaining insight in a web user’s personality is very valuable for applications that rely on personalisation, such as recommender systems and personalised advertising. In this paper we explore the use of machine learning techniques for inferring a user’s personality traits from their Facebook status updates. Even with a small set of training examples we can outperform the majority class baseline algorithm. Furthermore, the results are improved by adding training examples from another source. This is an interesting result because it indicates that personality trait recognition generalises across social media platforms.
SMALTA: Pushing the Storage Envelope with Prefix Lookups

For every IP datagram an Internet router needs to forward, specialized hardware performs a lookup through the forwarding table (FIB), a table that holds routes to destination prefixes. The number of such prefixes, and hence the FIB size, is growing at a fast pace, often requiring service providers to perform expensive memory upgrades. Prefix lookup schemes, which primarily attempt to minimize the lookup and update times, also allay concerns about the FIB memory size, at least partly, by employing efficient storage techniques. These lookup schemes, however, c the FIB as a word of God and do not attempt to alter its inherent structure. We present SMALTA 1 , a mechanism that downsizes the forwarding table by altering its structure. SMALTA reduces the number of FIB entries— the destination prefixes and their next hops—by means of aggregation before handing them over to the lookup scheme, without requiring any changes to the router hardware or to the routing protocols. SMALTA gracefully caters to the routing updates, while processing them in the routing table (RIB) and incrementally downloading them to the FIB. Accurately incorporating these updates requires modification of a little more than one RIB entry per update, on average, for practical tables obtained from routeviews [20]. From a routing information snapshot of 160818 prefixes taken from June 2004 routeviews data, SMALTA reduced these to 66.5% fewer prefixes. The reduction remained greater than 64% even after incorporating 86904 updates. This translates to roughly 30% savings in the storage required by Tree Bitmap, a popular and highly storage-efficient lookup scheme. Besides helping the lookup schemes save on storage, and potentially on lookup times, SMALTA also complements recent developments in route caching [18] and virtual aggregation [2], while seamlessly working with RIB reduction techniques. SMALTA uses a previously-known algorithm for performing optimal aggregation of the forwarding table [7], while allowing updates to be incorporated directly into the aggregated table. In order to test the routing correctness of SMALTA on a given forwarding table and set of updates, we develop and use a generic procedure which verifies if the forwarding behavior of the two forwarding tables is exactly the same for every possible IP address in the entire IP space.
Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul

Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period.
(Il)licit Economies in Brazil: An Ethnographic Perspective

Illicit economies are an issue of paramount importance and an opportunity for social mobility for millions in Brazil. The literature about them lacks empirical accuracy and less normative interpretive keys. Based on field research conducted between 2005 and 2018, this paper explores two stories: i) that of a young man working for illegal markets in the outskirts of Sao Paulo; and ii) that of a Toyota Hilux he stole. It adopts an approach centered on a theory of everyday action and focused on the boundary between legal and illegal and its pragmatic social effects. I argue the lack of public regulation of illicit economies has, over the last few decades, prevented their actors from obtaining social rights and started a vicious cycle of violence and reproduction of inequalities on a social level, as well as given rise to criminal populism in the public arena. As economias ilicitas no Brasil sao um problema publico de primeira relevância e uma oportunidade de mobilidade social para milhoes. A literatura a respeito carece de precisao empirica e de chaves menos normativas de interpretacao. Este artigo apresenta duas trajetorias, amparadas em pesquisas de campo realizadas entre 2005 e 2018: i) a de um jovem trabalhador de mercados ilegais das periferias de Sao Paulo; ii) a de uma Toyota Hilux roubada por ele. A abordagem, centrada numa teoria da acao cotidiana, toma como objeto a fronteira legal-ilegal e seus efeitos sociais pragmaticos. Argumenta-se que a ausencia de regulacao publica das economias ilicitas, nas ultimas decadas, impede contrapartidas sociais aos seus operadores e ativa socialmente um ciclo vicioso de violencia e reproducao de desigualdades, bem como o populismo penal na cena publica. Publisher's Note: This article has been published in both Portuguese and English. To download the Portuguese version, click the "Download" link and select "PDF (PT)". Este artigo foi publicado tanto em Ingles, como em Portugues. Para baixar a versao em Portugues, clique "Download" e depois selecione "PDF (PT)".
The Post-Failure Thoughts' in Mathematics Problems Solving Amongst Gifted and Normal Pupils

This study aims at investigating the post-failure thoughts' in mathematics' problems solving amongst gifted and normal pupils in the Middle School in Ouargla- Algeria. The study adopted the data collection on the mathematics teacher's appointments, and the Raven intelligence test to identify the gifted and the normal pupils, Also use the mathematical problems, and open questionnaire to determine the post-failure thoughts' in problem solving in mathematics. The sample consisted of (79) pupils in middle school. The outcomes showed: 1 - The post-failure thoughts' are: the thoughts of challenging, and thoughts of stopping. 2. There are significant differences at 0.05 in the thoughts of challenging between gifted and normal pupils favor the gifted pupils.
When is it Beneficial to Provide Freelance Suppliers with Choice? A Hierarchical Approach for Peer-to-Peer Logistics Platforms

Abstract This paper proposes and evaluates a new hierarchical approach to peer-to-peer logistics platforms, recasting the platform’s role as one providing personalized menus of requests to freelance suppliers. A bilevel optimization formulation explicitly models the two stage decision process: first, the platform determines which set of requests to recommend to which suppliers, and second, suppliers have a choice to select which request (if any) to fulfill. By harnessing the problem’s structure, the computationally expensive mixed-integer linear bilevel problem is transformed into an equivalent single-level problem that is computationally superior. A computational study based on ride-sharing quantifies the value of providing suppliers with choices. When a platform’s knowledge of suppliers’ selections is imperfect, our hierarchical approach outperforms existing recommendation methods, namely, centralized, many-to-many stable matching, and decentralized approaches. We show that a platform’s lack of knowledge over suppliers’ selections can be compensated by providing choices in environments with either inflexible suppliers or when suppliers’ utilities have higher variance than the platform’s utilities. In these environments, providing choices and recommending alternatives to more than one supplier can be beneficial to not only the platform, but also freelance suppliers and demand requests.
DESCRIBING THE VERTICAL STRUCTURE OF INFORMAL SETTLEMENTS ON THE BASIS OF LIDAR DATA – A CASE STUDY FOR FAVELAS (SLUMS) IN SAO PAULO CITY

Abstract. Cadastral mapping of favela’s agglomerated buildings in informal settlements at Level of Detail 1 (LoD1) usually requires specific surveys and extensive manual data processing. Therefore, there is a demand for including the favelas in the city map production on the basis of Lidar surveys, as well as the detection of their vertical growth. However, the currently developed algorithms for automatically extracting buildings from airborne Lidar data have mainly been tested only for regular building reconstruction. This study aims to develop a Lidar data processing pipeline enabling to compute metrics related to intraurban informal settlements. To do so, we present a procedure to generate favela’s buildings delineation, height, floors’ number and built area and apply them to six case studies in favela typo-morphologies. We conducted an exploratory analysis in order to obtain the adequate parameters of the processing pipeline and its evaluation, using open source, free license and self-developed software. The results are compared to reference data from the manual stereo plotting, achieving a quality index in the building reconstruction about 70%. We also calculated the growth density, measured by gross Floor Area Ratio index inside settlement, revealing values from 29% to 74% considering different time periods.
Depth Camera Based Image Processing for Indoor Mobile Robots Localization and Navigation

The increasing development of mobile robotic platforms able to perform activities such as localization, navigation or even map building in human shared environments has been propelling research areas such as Mobile Robotics, Cooperative Robotics, Human-Machine Interaction, etc. This issue is especially relevant when there are conditions to create an own mobile robotic platform from the scratch, making a robust system that serves internally for research purposes and perhaps to be placed on the market later on. The social robot, Interbot, is a mobile robotic platform developed by the ISR for indoor environments deployment. In this dissertation an image processing and acquisition module, more specifically depth images is proposed, with the aim of providing useful localization and navigation tools for this platform. In terms of hardware, it is used a Microsoft Kinect One sensor properly attached to the Interbot platform. Hereupon, a low complexity depth image processing system was developed. The system acquires the surrounding environment information using the Microsoft Kinect One sensor, this data is subjected to a first stage of processing, where the planar structures of the image (image planes) and their information (plane’s equation, normal to plan and points within the plane) are extracted, then a second processing phase where an obstacle laser scan is generated based on the preprocessed data. The user has the ability to easily adjust the system configuration parameters depending on the environmental characteristics where he wants to use the system and depending on the desired purpose. All proposed algorithms are developed and tested in MatLab and ROS (Robot Operating System). Keyword: Robot, Cooperative Robotic, Social Robotics, Navigation, Localization, ISR, ROS, MatLab, Mobile Robotic Platform, Microsoft Kinect One, Plane Detection, Plane Extraction, Depth Image, Depth Image Processing.