Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF TWO MODES OF COMPUTER AIDED INSTRUCTION ON STUDENTS' ACHIEVEMENT AND INTEREST IN STATISTICS AND PROBABILITY

The study investigated the effects of two modes of Computer Aided Instruction in Mathematics (CAIM) on students’ achievement and interest in Mathematics. A non-equivalent quasiexperimental research design was adopted in the study. Specifically, the study adopted pre-test posttest control group design. The study was carried out in Enugu Education zone using a random sample of 250 senior secondary school two (SSS2) students. Two groups were taught using Computer Aided Instruction in Mathematics; the treatment group was taught using drill and practice method and the control group were taught using tutorial method. The study was guided by four research questions and four null hypotheses tested at 0.05 level of significance. Data for the achievement in mathematics were collected using Statistics and Probability Achievement Test (SPAT) while data on interest were collected using Statistics and Probability Interest Inventory (SPII). Research questions were answered using mean and standard deviation while hypotheses were tested using analysis of co-variance (ANCOVA). The findings reviewed that students performed better as well as show more interest in solving mathematics problems when the drill and practice method of Computer Aided Instruction in Mathematics (CAIM) was used then when the tutorial approach was used. Again, the finding revealed that practicing at the computer terminals stimulate students more and helps them retain what they learnt longer, among other issues. It was recommended that government should organize mass computer literacy programme for secondary school mathematics teachers as well as providing adequate number of computers to schools for effective mathematics instruction with computers.
Computational geometry: algorithms and applications

The polygon in which geometric objects points. This is on geometry but this book can. Algorithm they consider and exercises but, these string matching algorithms data structures for those books. Computational geometry is clear and geographic information systems it includes some. Teaching layered range trees for self, study by obtaining a bachelor's. Randomized algorithms which varies over at, all main text. For many application areas all the dynamically changing set. In computational geometry are often quite hard to copy. In chapter which is to computational geometry. Springer ebooks it presents no real source code. For those people in parts of points the problems book. Not to a recognized discipline can, directly download your ebooks! Unlike most efficient and others in, this introduction to switch between. I find them for a, basic background. For geometric search problems studied and, hence the material covered at dallas. Each chapter the smallest distance from, begins with a first use solutions? This may be used to copy and algorithms as laptops. Readers are not regarded as well accepted introduction.
A Novel Fairness-Aware Resource Allocation Scheme in Multiuser SISO-OFDMA Downlink

The problem of resource allocation for the downlink of wireless systems operating over a frequency-selective channel is investigated. It is assumed that both the Base Station (BS) and each user are equipped with a single antenna (Single Input Single Output-SISO case), and Orthogonal Frequency Division Multiple Access (OFDMA) is used as a multiple access scheme. The aim is to maximize the sum of the users' data rates subject to constraints on total available power and proportional fairness among users' data rates. Achieving the optimal solution has a high computational cost thereby the use of suboptimal techniques is necessary. A suboptimal, but efficient, scheme is devised, and it is shown, via simulation, that not only the proposed resource allocation scheme achieve higher sum of the users' data rates than other existing schemes but also the sum data rate is distributed fairly and flexibly among users. In addition, the proposed scheme is complexity effective and can be applied to latest-generation wireless systems that provide Quality-of-Service (QoS) guarantees.
Author Identification in Malayalam using n-grams

Author identification is the problem of identifying the author of an anonymous text or text whose authorship is in doubt from a given set of authors. The works by different authors are strongly distinguished by quantifiable features of the text. This paper deals with the attempts made on identifying the most likely author of a text in Malayalam from a list of authors. Malayalam is a Dravidian language with agglutinative nature and not much successful tools have been developed to extract syntactic & semantic features of texts in this language. We have done a detailed study on the various stylometric features that can be used to form an authors profile and have found that the frequencies of word collocations can be used to clearly distinguish an author in a highly inflectious language such as Malayalam. In our work we try to extract the word level and character level features present in the text for characterizing the style of an author. Our first step was towards creating a profile for each of the candidate authors whose texts were available with us, first from word n-gram frequencies and then by using variable length character n-gram frequencies. Profiles of the set of authors under consideration thus formed, was then compared with the features extracted from anonymous text, to suggest the most likely author.
Backstepping Glycemic Control of Type 1 Diabetes for Implementation on an Embedded System

Abstract: In this paper a nonlinear control intended for Blood glucose regulation for Type 1 Diabetes patients is considered. The control law is designed using the Lyapunov theory associated with a Kalman filter to estimate the system states. The asymptotic stability of the overall system is theoretically proven. The proposed control scheme will be implemented in a low cost embedded system. The simulation results confirm the effectiveness of the proposed control.
BIGSCALE : AUTOMATIC SERVICE PROVISIONING FOR HADOOP CLUSTERS

As the number of interconnected devices grows in the IoT space, data processing systems require increased resources, robustness and flexibility. In this sense the scalability of a system becomes very important. A scalable system can process variable data volumes, requires less costs for maintenance and allows for fault tolerance and high availability. While horizontal scalability is offered by multiple Cloud providers, vertical scalability is a less addressed topic. In this article we first define the meaning and outline the benefits of doing vertical scalability. We also present a scaling solution which can automatically provision services based on the needs and resource usage of the system.
Modified SVPWM technique for a sensorless controlled induction motor drive using neural network observer and predictive controller

The use of multi-level inverter in a sensorless control scheme increases reliability in state parameter estimation. In this paper, sensorless control is presented using a neural network observer that uses the direct and quadrature current and voltage components for speed estimation. Distortion in current and voltage will result in deviations in speed estimation. In order to address the problem, this paper presents a modified space vector modulation scheme for sensorless control of induction motor drive fed by a multi-level inverter. The modulation scheme uses lesser switching states and is employed on a cascaded H-bridge inverter configuration. This results in reliable speed estimation by reducing distortion in current and voltage measurement. Moreover the paper uses predictive controller for speed control. Simulation is carried out in MATLAB and results show improved performance of sensorless operation.
Efficient dynamic simulation of multiple chain robotic systems

Oversize materials (e.g., maps, drawings, charts) are re­ produced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. These are also available as one exposure on a standard 35mm slide or as a 17" x 23" black and white photographic print for an additional charge.
Improving Semantic Image Segmentation by Object Localization

Semantic segmentation is about classifying every pixel in an image. In recent years, methods based on Fully Convolutional Networks (FCN) have dominated this field in terms of segmentation accuracy. We are interested in tackling the challenges that these methods are faced with. First, it is expensive to acquire pixel level labels to train the network. Second, FCN often has trouble with data that present imbalanced positive and negative samples. This issue often comes up in domains such as medical imaging and satellite imagery analysis, where the object of interest can be very small. The large number of negative samples can overwhelm the positive samples during training, leading to a biased representation learned by the network. In this thesis, we investigate how an object localization mechanism can address these two challenges. We propose an end-to-end neural network that improves the segmentation accuracy of FCN by incorporating an object localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. The two steps share convolutional features. This allows us to leverage object detection labels to help with the training of the segmentation network, alleviating the need for large-scale pixel level labels. To avoid applying max pooling on object proposals that limits the spatial accuracy, we introduce a new type of convolutional layer named ROI convolution. It applies convolution directly on the object proposals in one shot, without the need of passing them individually through the downstream network. We show that this layer is differentiable therefore allowing ii the network to be trained end-to-end. To demonstrate the efficacy of our method, we apply it to the problem of medical image segmentation. With the object localization unit, our method performs well despite the high class imbalance and it outperforms existing methods on small object segmentation. To understand further about the proposed method and the impact of ROI convolution, we also conducted ablation studies and experimented on an endoscopic image dataset with balanced data.
Image fusion using multiresolution analysis

The objective of this study is to propose multiresolution data fusion scheme using multiresolution analysis (MRA). Three techniques compared, respectively based on Haar wavelet basis (HWB), Daubechies wavelet basis (DWB), and IHS transform. Daubechies wavelet basis (DWB) is far more efficient than the HWB and IHS transform, thus establishing the advantages for data fusion of formally multiresolution analysis. DWB is best for image sharpening and to maintain the information of original data. Especially, Daubechies wavelet basis is able to enhance image sharpening and preserve spectral information.
Analysis on swarm robot coordination using fuzzy logic

In this paper, coordination among individual of swarm robot in communicating to maintain the safe distance between robots was analyzed. Each robot coordinates their movements to avoid obstacles and moving simultaneously. Evaluation of swarm robot performance is analyzed in this paper, namely: the coordination among robots to share information in safe distance determination. In controlling the coordination of motion, each robot has a sensor that provides several inputs about its surrounding environment. Fuzzy logic control in this paper allows uncertain input, and produces unlimited commands to control motion direction with speed settings according to environmental conditions. In this experiment, it is obtained that the size of the environment affects the coordination of robots.
Analysis of iterative procedure of matching two drawings by DP matching and estimation of time-variant transformation parameters

In signature verification and/or character recognition problems, matching of two drawings has a certain significance. For these problems, online equipments such as a tablet and an electronic pen are simultaneously used. In an environment where online equipment can be used, using these is more efficient than simply using a pen and a sheet of paper. The latter is called 'offline method'. Two advantages of using online equipments arise from here. One is that the analyser is free from the heavy computational load of image processing which is inherent to offline methods. Thus the analysis can also be 'online' without waiting the availability of the whole signature. The other is that the analyser can use various information of writing which is usually unavailable in offline method. An example can be shown in the signature verification. If the holder of the signature intentionally writes a part of the signature in a strange direction, others are not likely to be able to imitate it. Another simple technique for makin
Knowledge management methodology for identifying threats in maritime/logistics supply chains

ABSTRACT The growing complexity and the heterogeneity of critical infrastructures (CIs) in multicultural maritime and logistics networks challenge existing methods and tools to dynamically respond to the frequent change of information and to the lack of efficiently sharing security knowledge over the supply chain. This fosters a semantic gap, which causes disintegration in the supply-chain workflow and attracts cyber-attackers attention. This paper proposes a knowledge management methodology and an associated tool for the maritime logistics and supply chain (MLoSC), which aims to enable the sharing of supply chain knowledge and suggests ways for identifying cyber threats over CIs. The methodology is illustrated via an indicative service (the vehicle transport service), examined in the context of three prominent maritime use cases. The proposed methodology is used to develop a knowledge base for the MLoSC using semantic web technologies.
An MMSE fine carrier frequency synchronization algorithm for OFDM systems

In this paper, we describe a patent pending, fine carrier frequency synchronization algorithm based on a minimum mean-squared error (MMSE) approach for OFDM systems which is insensitive to symbol timing offsets. In order to obtain these attributes, the proposed algorithm adopts a cost function similar to the CMA (constant modulus algorithm) used for blind channel equalization. Another advantage of the algorithm is that the transmission of a known training pattern is unnecessary, thus, improving the transmission efficiency. A hardware efficient version of the proposed algorithm is also developed. Computer simulations are performed to evaluate the performance with QPSK and 64-QAM modulations under a frequency selective multipath channel.
Language Processing and Knowledge in the Web: 25th International Conference, GSCL 2013, Darmstadt, Germany, September 25-27, 2013, Proceedings

This book constitutes the refereed conference proceedings of the First International Conference on Language Processing and Knowledge in the Web, GSCL 2013, held in Darmstadt, Germany, in September 2013. The 20 revised full papers were carefully selected from numerous submissions and cover topics on language processing and knowledge in the Web on several important dimensions, such as computational linguistics, language technology, and processing of unstructured textual content in the Web.
The Formation of Laboratory Analogues of Natural Hydrate-Bearing Fine-Grained Soil

Gas hydrates form in deep-water marine sediments, and have been suggested as being a geohazard when temperature or pressure changes result in hydrate dissociation, with fine-grained hydrate-bearing sediments posing the greatest potential risk. Recent drilling expeditions with improved sampling techniques have revealed that within fine-grained soils, hydrates form complex, sub-vertical veins. These veins are hypothesized to lead to an increase in the strength and stiffness of the hydrate-bearing soil. However, current understanding of the strength and deformation behaviour of hydrate-bearing sediments is limited to laboratory studies in which hydrate is formed within the pore space of sediments. This paper presents a detailed procedure in which vertical, cylindrical synthetic hydrate veins are formed within a consolidated laboratory-mixed fine-grained soil, and are then isotropically reconsolidated within a triaxial cell. Results indicate that hydrate veins remain intact throughout this process. RÉSUMÉ Les hydrates de gaz se forment dans les sédiments des fonds marins. Les sédiments à grains fins qui renferment des hydrates peuvent poser un risque si des changements de température ou de pression provoquent la fonte des hydrates. Des expéditions de forage ont révélé que les hydrates forment des veines complexes et sub-verticales dans les sédiments à grains fins. L'hypothèse est que ces veines d’hydrates causent une augmentation de la résistance et de la rigidité du sédiment. Cependant, notre compréhension de la résistance et du comportemtent en déformation des sédiments contenant des hydrates est limitée à des études en laboratoire lors desquelles les hydrates se forment dans les pores du sédiment. Cet article présente une procédure détaillée pour former des veines synthétiques verticales et cylindriques d'hydrates dans un sédiment à grain fin. Les échantillons sont ensuite reconsolidés sous pression isotrope dans une cellule triaxiale. Les résultats indiquent que les veines d’hydrates restent intactes tout au long de la procédure.
Essays on hyperspectral image analysis : classification and target detection

Over the past a few decades, hyperspectral imaging has drawn significant attention and become an important scientific tool for various fields of real-world applications. Among the research topics of hyperspectral image (HSI) analysis, two major topics -- HSI classification and HSI target detection have been intensively studied. Statistical learning has played a pivotal role in promoting the development of algorithms and methodologies for the two topics. Among the existing methods for HSI classification, sparse representation classification (SRC) has been widely investigated, which is based on the assumption that a signal can be represented by a linear combination of a small number of redundant bases (so called dictionary atoms). By virtue of the signal coherence in HSIs, a joint sparse model (JSM) has been successfully developed for HSI classification and has achieved promising performance. However, the JSM-based dictionary learning for HSIs is barely discussed. In addition, the non-negativity properties of coefficients in the JSM are also little touched. HSI target detection can be regarded as a special case of classification, i.e. a binary classification, but faces more challenges. Traditional statistical methods regard a test HSI pixel as a linear combination of several endmembers with corresponding fractions, i.e. based on the linear mixing model (LMM). However, due to the complicated environments in real-world problems, complex mixing effects may exist in HSIs and make the detection of targets more difficult. As a consequence, the performance of traditional LMM is limited. In this thesis, we focus on the topics of HSI classification and HSI target detection and propose five new methods to tackle the aforementioned issues in the two tasks. For the HSI classification, two new methods are proposed based on the JSM. The first proposed method focuses on the dictionary learning, which incorporates the JSM in the discriminative K-SVD learning algorithm, in order to learn a quality dictionary with rich information for improving the classification performance. The second proposed method focuses on developing the convex cone-based JSM, i.e. by incorporating the non-negativity constraints in the coefficients in the JSM. For the HSI target detection, three approaches are proposed based on the linear mixing model (LMM). The first approach takes account of interaction effects to tackle the mixing problems in HSI target detection. The second approach called matched shrunken subspace detector (MSSD) and the third approach, called matched cone shrunken detector (MSCD), both offer on Bayesian derivatives of regularisation constrained LMM. Specifically, the proposed MSSD is a regularised subspace-representation of LMM, while the proposed MSCD is a regularised cone-representation of LMM.
A novel approach for grid resource management based on fuzzy logic and semantic Technology

one of the important challenges in grid is quality of service (QoS) for all the accepted users or applications, while increasing the utilization of resources through resource sharing. An effective resource management approach must guarantee the QoS and balance the load among grid nodes which are frequently highly dynamic and heterogeneous. Grid resources are commonly varied regarding their software and hardware formations. Collecting and supervision of these resources, and discovering appropriate resources is an important topic. So semantic can be very useful for resource discovery and resource selection. Fuzzy theory is another intelligent approach which shows uncertainty in Phenomena. In this paper, a novel approach, which is using semantic and fuzzy theory, is proposed to discover grid resource in Grid environment. Search expressiveness, efficiency and scalability, precision and also discover more related resources are the characteristics of the proposed framework.
Facilitating collaboration: exploring a socio-technical approach to the design of a collaboratory for Library and Information Science

The thesis explores the potential of one way of facilitating and stimulating collaboration in Library and Information Science (LIS), through a specific scientific collaboration activity: creating, sharing and reusing data collection instruments, such as interview guides, questionnaires, and observation protocols. The four studies reported in the thesis can be read as a linear narrative, each study building on the previous and contributing to the following ones. Together the four studies describe the process exploring social and contextual aspects of LIS; developing requirements and designing a working prototype collaboratory; and evaluating how the prototype collaboratory was perceived by LIS professionals. Overall, the results show that whereas the benefits of an LIS collaboratory reported by the study participants focused on the greater good for LIS, the challenges reported focused on the individuals’ perspectives. Hence, a tension exists between supporting the greater good, and challenges for individuals concerning sharing and reusing data collection instruments in an LIS collaboratory. The thesis emphasizes the implications for the LIS discipline when new ways of working with data collection instruments would be introduced; the implications of addressing needs of a diverse target audience; and the implications for further design iterations of an LIS collaboratory, including rewarding contributions, and ensuring quality content in a collaboratory.