Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual Representations in Mathematics Teaching: An Experiment with Students.

General problem-solving skills are of central importance in school mathematics achievement. Word problems play an important role not just in mathematical education, but in general education as well. Meaningful learning and understanding are basic aspects of all kinds of learning and it is even more important in the case of learning mathematics. In order to efficiently enhance students' problem solving skills they should be assigned world problems which are new to them and to which they themselves have to find the steps to the solution, the algorithm. A number of researches, experiments and scientific papers in the didactics of mathematics prove that the role of visual representations in word problem solving is essential. Visual representation often helps in understanding a problem. Using visual representations leads to a better understanding and to improving special mathematical reasoning. The present paper presents an experiment involving university students who have been asked to solve a logical problem. There were three experimental groups. The aim was to find out whether problem solving is more efficient when using the traditional method (working on paper, with pencil) or when using a model.
Neuro-Based Thumb-Tip Force and Joint Angle Modelling for Development of Prosthetic Thumb Control

Human fingers have a specific role that contributes to different hand functions. Among these fingers, the thumb plays the most special function as an anchor to many hand activities. As a result, the loss of the thumb due to traumatic accidents can be catastrophic as proper hand function will be severely limited. In order to solve this problem, a prosthetic thumb is developed to be worn in complementing the function of the rest of the fingers. The movement of the prosthetic device can be naturally controlled by using electromyogram (EMG) signals. In this work, the EMG signals from the human muscles were measured in different thumb configurations and thumb-tip forces in flexion movement. The muscles involved are the Adductor Pollicis (AP), Flexor Pollicis Brevis (FPB), Abductor Pollicis Brevis (APB) and First Dorsal Interosseous (FDI). The classification of the EMG signals based on different force and thumb configurations is performed using an Artificial Neural Network (ANN). From a series of experiments, the results show that the neural network efficiently classified the signals and a unique set of EMG signals was generated for each thumb movement and force. Therefore, EMG signals were used to control the prosthetic movement with aid from the developed neural network.
Firm performance effects in relations to the implementation and use of knowledge management systems

Knowledge Management Systems (KMS) are IT-based systems developed to support the organisational processes of knowledge creation, storage, and application. While sizeable investments in KMS are being made, practitioners are struggling to determine whether these expenditures improve the firm performance. The results indicate that not only does the adoption of KMS decrease administrative cost, but it also pays off in profitability. Furthermore, this study also examines the industry effect between manufacturing firms and service firms. Results indicate that administrative costs and profitability are significantly better for manufacturing firms, as compared to service firms. These findings will help both researchers and practitioners.
Heterogeneous control and data sub-network for millimeter-wave communications

Millimeter-wave (mmWave) communications are highly focused as a powerful mean enabling to perform very high data transmission. However it has several inherent shortcomings like directional transmission and serious attenuation in atmosphere. So it is difficult to implement random access in mmWave WLANs. In this paper, a heterogeneous control and data sub-network architecture is presented, which decouples the traditional WLAN into 2.4 or 5GHz control sub-network and mmWave data sub-network in both PHY and MAC layers. In control sub-network, DCF is adopted to transmit control information and in data sub-network, PCF is adopted to ensure the QoS. Moreover, an omnidirectional transmission is employed in the control sub-network to support users� random access. The data sub-network only covers the required serving area by using directional antennas for specific users and can be adjusted dynamically based on control information. Simulations indicate that compared with the conventional WLANs, heterogeneous mmWave WLANs can provide both random access and high throughput.
The TRAINS 93 Dialogues

This report describes a corpus of task-oriented dialogues set in the TRAINS domain. A user collaborates with a planning assistant to accomplish some task involving manufacturing and shipping goods in a railroad freight system. We include a description of the task, collection situation, and transcriptions conventions. The audio files, along with time-aligned word and phoneme transcriptions are available on CD-ROM from the Linguistic Data Consortium. Altogether, there are 98 dialogs included, collected using 20 different tasks and 34 different speakers. This amounts to six and a half hours of speech, about 5900 speaker turns, and 55000 transcribed words.
Using sentiment analysis & machine learning for security price forecasting

We worked with sentiment analysis and supervised machine learning to forecast the security movements in stock market and benefit from it. Text rich data sources like newspapers, blogs, stock market related internet forums, social networking websites contain relevant and updated information about the publicly listed companies. Sentiment analysis can help us to extract usable information from these texts to understand the overall sentiment of the articles. In our research, we used two sentiment analyzed database provided by Accern [1] & Sentdex [19] and tried to see how positive is the relation between the market sentiment and market movement of S&P 100 index listed companies. We also implemented machine learning agent trained on the price data to find a comparable result. With our implementation we have been able to consistently perform better than the benchmark with low beta and sharpe which suggests that algorithms based on state­of­the­art sentiment analyzed data can follow the market movement stably. We have also seen that machine learning agent trained on the price data can move with the market given a higher initial investment.
ROC Analysis Applied to Multivariate Diagnostic Tests

The area of the relative operating characteristic (ROC) curve provides a nonparametric index of diagnostic test performance.1 The area of the ROC curve is criterion-free and corresponds to the discriminability of patient test scores from normal test scores. That is, the area of the ROC curve is equivalent to the probability of correctly identifying patient test results in a two-alternative forced-choice decision task. Because of this equivalence, ROC analysis provides for the comparison of subjective clinical judgments with quantitative test results, using the same statistic.1-2
Atelier Infrastructure for Ubiquitous Computing

Ubiquitous computing systems, running on distributed heterogeneous hardware and operating system platfoms, need a software infrastructure hiding the complexity of the underlying hardware, software and networking technologies form the components and applications. A software infrastructure for ubiquitous computing should enable software components and applications consisting of these components to communicate over different types of networks, using different types of protocols. We describe an infrastructure called Atelier Infrastructure for ubiquitous computing, aiming at supporting the application developers in building distributed ubiquitous applications in heterogeneous hardware, operating system and programming environments. The Atelier Infrastructure addresses the abstraction, programming language independence, extendability and configurability requirements for an ubicomputing infrastructure by a message based communication and a distributed micro kernel software architectural pattern. We suggest that future research should be directed towards specifying open, common standards to enhance the interoperability of components, services and applications in ubiquitous environments.
The effectiveness of NGO-sponsored insert on the classroom practices of primary school mathematics educators: a case study of MCPT

In this research report, the effectiveness of NGO-sponsored mathematics INSET on the classroom practices of primary school educators is investigated. The study analyses the classroom practices of ten Mathematics Centre for Primary Teachers (MCPT) trained educators. This analysis takes place from the stated goals of the MCPT programme on the one hand, and from a theoretical analysis of the construction of knowledge on the other. This qualitative study, which made use of structured observation schedules and interviews of ten MCPT-trained educators, revealed interesting trends in their practice in relation to MCPT training. While educators reflected ranging strategies, direct teaching and repetitive practices were marginalised, raising important issues for curriculum reform in South Africa, When employing more open methods, limits to educators' content knowledge and indiscretion in the use of resources were revealed. In a context of limited resources in South Africa, and the significance of NGO sector, in assisting with curriculum development and change, these insights have significance beyond MCPT.
FEATURE INTEGRATION FOR IMAGE INFORMATION RETRIEVAL USING IMAGE MINING TECHNIQUES

In this image information retrieval system the feature extraction process is analyzed and a new set of integrated features are proposed using image mining techniques. The content of an image can be expressed in terms of low level features as color, texture and shape. The primitive features are extracted and compared with data set by using various feature extraction algorithms like color histograms, wavelet decomposition and canny algorithms. This paper emphasizes on feature extraction algorithms and performance comparison among all algorithms. The system uses image mining techniques for converting low level semantic characteristics into high level Features, which are integrated in all combinations of primitive features. The feature integration has restricted to three different methodologies of feature Integration: color and texture, color and shape, texture and shape. It is ascertained that the performance is superior when the image retrieval based on the super set of integrated features, and better results than primitive set.
Mobile Robot Navigation

Robots will soon take part in everyone’s daily life. In industrial production this has been the case for many years, but up to now the use of mobile robots has been limited to a few and isolated applications like lawn mowing, surveillance, agricultural production and military applications. The research is now progressing towards autonomous robots which will be able to assist us in our daily life. One of the enabling technologies is navigation, and navigation is the subject of this thesis. Navigation of an autonomous robot is concerned with the ability of the robot to direct itself from the current position to a desired destination. This thesis present and experimentally validates solutions for road classification, obstacle avoidance and mission execution. The road classification is based on laser scanner measurements and supported at longer ranges by vision. The road classification is sufficiently sensitive to separate the road from flat roadsides, and to distinguish asphalt roads from gravelled roads. The vision-based road detection uses a combination of chromaticity and edge detection to outline the traversable part of the road based on a laser scanner classified sample area. The perception of these two sensors are utilised by a path planner to allow a number of drive modes, and especially the ability to follow road edges are investigated. The navigation mission is controlled by a script language. The navigation script controls route sequencing, junction detection, junction crossing calculations and drive mode selection. The entire system is tested on a combination of narrow asphalt and gravelled roads connected by a number of junctions. Missions of up to 3 km in length have been successfully completed using the described system. The main focus of the thesis has been the experimental validation of the implemented solutions and the ability of the methods to solve real world problems. The amount of software needed by an autonomous robot can be overwhelming. Software reuse and distributed development are therefore important issues. The thesis describes a new component architecture for robotics software that promotes software reuse and distributed development and maintenance.
Architecture for Web-Based Image Mining and Management Systems

The explosive growth in the volume of images available on the Web and in enterprise databases continues unabated. However, images on the Web are of many variations in type and format. Selecting relevant images based on image content is a difficult problem. Managing a large amount of images using their semantic meaning with content-based retrieval remains a challenge for both database and image processing researchers. This paper contributes by proposing an architecture for Web-based image mining and management systems with content-based image retrieval (CBIR), ImgWeb. ImgWeb is a Web-based system because it not only enables image mining and management for images in the database but also images from the Internet (Web images). Image agents play a key role in retrieving and mining Web images that are relevant to textual queries. ImgWeb is an object-oriented image database system, and it represents images as content-objects for processing. Thus, it supports CBIR and a wide range of graphical and textual querying methods. The proposed architecture permits the development of new image mining and management systems by utilizing existing components. Examples of image database applications include graphic design, recognition and entertainment.
Models of computation: A numeric analysis and performance evaluation

This research seeks to better understand what drives performance in computation. To develop this understanding the researcher investigates the literature on computational performance within the classical and quantum paradigm for both binary and multi-value logic. Based on the findings of the literature the researcher evaluates through an experiment of addition what drives performance and how performance can be improved. For the evaluation of this research, a realist research paradigm employs two research methods. The first is an automaton model of computation to model each of the computing paradigms and computational logic. The second is computational complexity theory for measuring the performance of addition. Through this evaluation the researcher seeks to gain a better understanding of what drives computational performance and how addition can be performed more efficiently. The results of the research lead the researcher to conclude that modernisation of machinery caused the birth start of automated computing and the binary number system in computers. As this research indicated that computation through increasing the radix can improve performance of computation for addition. Based on reported findings in the science of quantum mechanics research, it would be possible to implement a model of computation with increased radix. Through embracing state discrimination/ distinguishability this research calls to review the current quantum computing paradigm based on state duality.
Determining the focus of instruction: content planning for intelligent tutoring systems

There has been a growing interest among intelligent tutoring systems researchers in providing an explicit instructional component that is responsible for determining what to do next at each point in the instructional interaction. Instructional planning is the process of mapping out a global sequence of instructional goals that provides consistency, coherence, and continuity throughout an instructional session. This thesis presents a novel approach to instructional planning by separating the planning process into two phases: content planning and delivery planning. Although the literature recognizes the distinction between the content of an instructional interaction and the mode of delivery, the majority of instructional planners developed to date have focussed on delivery aspects. This thesis is a detailed investigation into content planning as a precursor activity to delivery planning. One of the primary goals of this research has been to identify the dimensions of knowledge required to make pedagogical decisions, and to explicitly represent and reason about it in a planning paradigm. In order to make informed decisions that will tailor instruction to the individual student, the following types of pedagogical knowledge are required: (a) the concepts to be learned, and the prerequisite and other structural relationships between concepts (e.g. part of and isa); (b) the various abilities in using concepts such as knowing the definition of a concept, being able to analyze behaviour or synthesize a solution using a concept; (c) the typical misconceptions of the domain; and (d) the pedagogical rules that represent a pedagogical philosophy for learning the domain. By providing an explicit pedagogical view of the knowledge to be learned, it is possible to plan effective pedagogical interventions that will help the student to move from their current knowledge state to the next knowledge state that is closer to the desired goal state. The proposed approach, PEPE, is a framework for representing the pedagogical knowledge required to plan the content of instruction. A description of a content planner built on the PEPE approach is provided in the form of an extended example. This content planner uses the various types of pedagogical knowledge in conjunction with a student model to generate content goals, to generate a plan to achieve the content goals, to evaluate the effectiveness of the content plan as it is executed by the delivery planner, and lastly to react in the appropriate manner. An interpretation of PEPE illustrates the flexibility of the approach and highlights the role that content planning can play in a variety of tutoring styles.
Online Active Learning in Practice

We compare the practical performance of several recently proposed algorithms for active learning in the online setting. We consider two algorithms (and their combined variants) that are strongly online, in that they do not store any previously labeled examples, and for which formal guarantees have recently been proven under various assumptions. We perform an empirical evaluation on optical character recognition (OCR) data, an application that we argue to be appropriately served by online active learning. We compare the performance between the algorithm variants and show significant reductions in label-complexity over random sampling.
Conditional Lemma Discovery and Recursion Induction in Hipster.

Hipster is a theory exploration tool for the proof assistant Isabelle/HOL. It automatically discovers lemmas about given recursive functions and datatypes and proves them by induction. Previously, only equational properties could be discovered. Conditional lemmas, for example required when reasoning about sorting, has been beyond the scope of theory exploration. In this paper we describe an extension to Hipster to also support discovery and proof of conditional lemmas. We also present a new automated tactic, which uses recursion induction. Recursion induction follows the recursive structure of a function definition through its termina- tion order, as opposed to structural induction, which follows that of the datatype. We find that the addition of recursion induction increases the number of proofs completed automatically, both for conditional and equational statements.
A 0.9V, 30µW feature extractor for remote speech recognition

A feature extraction chip for speech recognition computes fifteen cepstra each 8ms at 64kHz clock rate and dissipates 30µW at 0.9V. It has been implemented as a gate array in a 0. 5µm, three-metal CMOS technology. The average energy required to process a single word of the TI46 speech corpora is 10µJ. It achieves recognition rates over 98% in isolated-word, speech recognition tasks.
A Study on Digital Image Steganography

Steganography is the art and science of invisible communication. The word steganography is derived from the Greek words "stegos" meaning "cover" and "grafia" meaning "writing" defining it as "covered writing". The existence of a message is secret. Steganography is usually implemented computationally, where cover Works such as text files, images, audio files, and video files are tweaked in such a way that a secret message can be embedded within them. In order to embed secret data into a cover message, the cover must contain a sufficient amount of redundant data or noise. This is because in the embedding process Steganography actually replaces this redundant data with the secret message. In image steganography the information is hidden exclusively in images. This paper intends to give an overview of image steganography.
Dynamic stopping in a calibration-less P300 speller

Even though the P300 based speller has proved to be usable by real patients, it is not a user-friendly system. The necesarry calibration session and slow spelling make the system tedious. We present a machine learning approach to P300 spelling that enables us to remove the calibration session. We achieve this by a combination of unsupervised training, transfer learning across subjects and language models. On top of that, we can increase the spelling speed by incorporating a dynamic stopping approach. This yields a P300 speller that works instantly and with high accuracy and spelling speed, even for unseen subjects.
MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems

Until now, design patterns for the MapReduce framework have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework youre using. Each pattern is explained in context, with pitfalls and caveats clearly identified to help you avoid common design mistakes when modeling your big data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data ""A clear exposition of MapReduce programs for common data processing patternsthis book is indespensible for anyone using Hadoop. "" --Tom White, author of Hadoop: The Definitive Guide