Miracle-dz/UniFAR-FTUs huggingface dataset:
Introduction to a new emphatic model for predicting patients and understanding mining techniques

Data mining is the act of inspecting substantial prior databases to create new data, choosing and exploring large volumes of information. This procedure has turned into an inexorably forceful activity in every aspect of health care and research. A lot of scattered data about health is accessible. This paper actualizes an imaginative thought to recognize ailments influenced in humans and gives the results in terms of accuracy. Hybrid algorithm approach of simple logistic and IB1 is used. The image set of the sick humans are caught with the assistance of high pixel cameras or cell phones like android, iPhone or remote PDA. These images are then nourished for application for distinguishing human ailments and recommend solutions. Result in terms of accuracy shows improvement by15% than existing algorithms.
Technical Oral Presentation Delivery: Qualitative Analysis of Engineering Undergraduates’ Impediments to Effective Oral Presentations in an Engineering Context

Engineering undergraduates are expected to deliver technical oral presentations on various occasions particularly in university subjects. Engineering undergraduates struggle when they are assigned to deliver technical oral presentations and often they have impediments to deliver oral presentations at tertiary level. The paper reveals prime findings on the technical oral presentation’s impediments faced by the engineering undergraduates in delivering effective presentations. A semi-structured interview was conducted by 12 engineering undergraduates from various engineering faculties in Universiti Malaysia Pahang, Malaysia. Findings of the study revealed that the engineering undergraduates encountered impediments such as when delivering technical oral presentations particularly in terms of presentation skills as well as English as a medium of instruction. In this study, the researchers provided, therefore, suggestions on how to improve the engineering undergraduates’ technical oral presentation skills.
Salient-motion-heuristic scheme for fast 3D optical flow estimation using RGB-D data

Optical flow is widely used for describing motion cues in the scene, but limited by slow estimating speed and illumination sensitivity. To handle both problems, this paper focuses on improving speed and accuracy of optical flow using RGB-D data and enhancing its robustness on motion description via fusing depth flow which is obtained only using depth data. First, salient motion regions (SMRs) are detected between depth frames which have good character on motion description for they all locate on moving objects. Then, depth flow is calculated to describe 3D motion for each SMR and directs fast orientation region growing on depth map. Thus larger motion regions are grown, and region-based optical flow estimation is conducted on grown regions. Estimation error is reduced and noise is inhibited due to depth constraints. Finally, a fusion scheme is adopted which combines depth flow and optical flow for better 3D motion description in the scene. Experiments on a RGB-D video data sets recorded in various complex scenes demonstrate the improved speed and robustness of the proposed method.
Semantic Role Labeling using Dependency Syntax

This document gives a brief introduction to the topic of Semantic Role Labeling using Dependency Syntax. We also describe a system that has been developed and tested on a corpus from the CoNLL-2008 1 shared task. We evaluate the system and give a short discussion on further improvements. Our results are reasonably good compared to those reached during the shared task.
A Two-Level Topic Model Towards Knowledge Discovery from Citation Networks

Knowledge discovery from scientific articles has received increasing attention recently since huge repositories are made available by the development of the Internet and digital databases. In a corpus of scientific articles such as a digital library, documents are connected by citations and one document plays two different roles in the corpus: document itself and a citation of other documents. In the existing topic models, little effort is made to differentiate these two roles. We believe that the topic distributions of these two roles are different and related in a certain way. In this paper, we propose a Bernoulli process topic (BPT) model which considers the corpus at two levels: document level and citation level. In the BPT model, each document has two different representations in the latent topic space associated with its roles. Moreover, the multi-level hierarchical structure of citation network is captured by a generative process involving a Bernoulli process. The distribution parameters of the BPT model are estimated by a variational approximation approach. An efficient computation algorithm is proposed to overcome the difficulty of matrix inverse operation. In addition to conducting the experimental evaluations on the document modeling and document clustering tasks, we also apply the BPT model to well known corpora to discover the latent topics, recommend important citations, detect the trends of various research areas in computer science between 1991 and 1998, and to investigate the interactions among the research areas. The comparisons against state-of-the-art methods demonstrate a very promising performance. The implementations and the data sets are available online .
Notice of Violation of IEEE Publication PrinciplesWorkload-Based Query Routing Tree Algorithm in Wireless Sensor Networks

Constructing an effective Query Routing Tree is the premise for continuous queries in a Wireless Sensor Networks (WSN). The query routing tree structures can provide sensors with a path to the querying nodes. At present, the data acquisition systems for WSN construct the routing structures in an ad-hoc manner, therefore, there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present a Workload-based Query Routing Tree construction (WQRT) algorithm, which balances the workload among nodes and minimizes the data collisions, thus reducing energy consumption in the course of data acquisition. The simulation experiments from Intel Research illustrate that the proposed WQRT can significantly reduce energy consumption under a variety of conditions and prolong the lifetime of a wireless sensor network.
Multivariate meta‐analysis: the effect of ignoring within‐study correlation

Summary. Multivariate meta‐analysis allows the joint synthesis of summary estimates from multiple end points and accounts for their within‐study and between‐study correlation. Yet practitioners usually meta‐analyse each end point independently. I examine the role of within‐study correlation in multivariate meta‐analysis, to elicit the consequences of ignoring it. Using analytic reasoning and a simulation study, the within‐study correlation is shown to influence the ‘borrowing of strength’ across end points, and wrongly ignoring it gives meta‐analysis results with generally inferior statistical properties; for example, on average it increases the mean‐square error and standard error of pooled estimates, and for non‐ignorable missing data it increases their bias. The influence of within‐study correlation is only negligible when the within‐study variation is small relative to the between‐study variation, or when very small differences exist across studies in the within‐study covariance matrices. The findings are demonstrated by applied examples within medicine, dentistry and education. Meta‐analysts are thus encouraged to account for the correlation between end points. To facilitate this, I conclude by reviewing options for multivariate meta‐analysis when within‐study correlations are unknown; these include obtaining individual patient data, using external information, performing sensitivity analyses and using alternatively parameterized models.
A frequency-based gene selection method with random forests for gene data analysis

Gene selection is an important step in analysis of gene data sets in which the number of genes exceeds greatly the number of samples. In this paper, we propose a new method that uses a random forest model to select genes from high dimensional gene data sets. In this method, Breiman's random forest algorithm is first used to generate a random forest model from a high dimensional data set. Then, features appearing in component tree models of the random forest are analyzed using the measures of feature correlations. Features are divided into two sets, those appearing in the roots of component trees and those appearing in other nodes of the trees. The frequency of the features is calculated and the features whose frequency is greater than given thresholds are selected as candidates. Finally, the correlation of candidate features with the class feature is measured with symmetrical uncertainty and the top features (with the highest symmetrical uncertainty values) are selected. 19 gene data sets were used to evaluate the new gene selection method. The comparison results have shown that the models built with the gene features selected with the new method outperformed other random forest models in classification accuracy.
Examining the Limits of Predictability of Human Mobility

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano’s inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability.
Pole-placement vs. loop-shaping design for gain-scheduling control of machine tools with position dependent dynamics

The high accelerations together with the high controller band-widths required from present-day machine tools are likely to excite the vibration modes of the machine structure. In order to achieve a high control bandwidth and high contouring accuracy, these structural eigenfrequencies need to be incorporated in the controller design. An additional complication with machine tools is that the structural eigenfrequencies are not constant but depend on the position of the tool in the workspace of the machine tool, with as consequence that the machine model is position dependent and therefore cannot be modelled as a single linear time-invariant (LTI) model. To control such linear time-varying (LTV) systems, two approaches are possible: (i) the controller is such that the system behaviour is largely independent from system parameter variations (robust control), and (ii) adapt the controller structure or parameters to the system parameter variations, e.g. by gain-scheduling. In this paper an experimental set-up, consisting of one axis of an industrial pick-and-place machine, driven by a linear motor, is controlled based on the gain-scheduling approach. The set-up contains a flexible arm of which the stiffness depends on its length. Pole-placement and loop-shaping controllers are designed for several constant arm lengths and these controllers are linearly scheduled in a global gain-scheduling controller. Experiments show that scheduling is necessary if high-performance controllers are demanded.
Evolutionary White-Box Software Test with the EvoTest Framework: A Progress Report

Evolutionary white-box software testing has been extensively researched but is not yet applied in industry. In order to investigate the reasons for this, we evaluated a prototype version of a tool, representing the state-of-the-art for evolutionary structural testing, which is targeted at industrial use. The focus was on the applicability of the structural test tool in the industrial context and not on assessment of the test cases generated. Four case studies, each consisting of an embedded software module from the automotive industry implemented in the C language, were evaluated with the tool. The case studies had to be customized to cope with the limitations of the tool and in all, test case generation succeeded for 37% of the functions selected for the evaluation. Weaknesses of the tool were reported to the developers and subsequently eliminated, resulting in a later version of the tool being able to process 82% of the selected case study functions. However, the study shows that significant engineering work is still required before evolutionary structural testing is ready for industrial application.
A computerized approach for morphological analysis of the corpus callosum.

OBJECTIVE A new technique for analyzing the morphology of the corpus callosum is presented, and it is applied to a group of elderly subjects. MATERIALS AND METHODS The proposed approach normalizes subject data into the Talairach space using an elastic deformation transformation. The properties of this transformation are used as a quantitative description of the callosal shape with respect to the Talairach atlas, which is treated as a standard. In particular, a deformation function measures the enlargement/shrinkage associated with this elastic deformation. Intersubject comparisons are made by comparing deformation functions. RESULTS This technique was applied to eight male and eight female subjects. Based on the average deformation functions of each group, the posterior region of the female corpus callosum was found to be larger than its corresponding region in the males. The average callosal shape of each group was also found, demonstrating visually the callosal shape differences between the two groups in this sample. CONCLUSION The proposed methodology utilizes the full resolution of the data, rather than relying on global descriptions such as area measurements. The application of this methodology to an elderly group indicated sex-related differences in the callosal shape and size.
COMBINING NEURAL NETWORKS VOTING CLASSIFIERS AND ERROR CORRECTING OUTPUT CODES

We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this distance. We compare one-inn and ECOC on a multiclass data set using standard MLPs and bagging and arcing voting committees.
An exploration of Cognitive Behavioural Therapists' personal and professional development

This thesis explores Cognitive Behavioural Therapists’ personal and professional development. Paper one consists of a systematic review of the quality of ten studies examining the measurement properties of CBT Competence Scales (CCSs). Overall the quality of the studies varied, but most were rated as ‘fair’ due to small sample sizes. The review also found inconsistency in the way some measurement properties were defined. The review concluded that more research into the measurement properties of CCSs is needed and that consensus is required to determine appropriate sample sizes and measurement property definitions. Paper 2 explored the personal and professional development of eight students over the course of a postgraduate certificate in Cognitive Behavioural Therapy (CBT), within Personal Construct Theory, using the repertory grid technique. The study found that certificate students perceived an increase in CBT competence but at the expense of interpersonal effectiveness. The study found difference in the personal and professional development of certificate CBT students, compared to previous findings with diploma students. Implications for students, trainers and commissioners are discussed, with suggestions on ways to increase certificate students’ reflection. Paper three presents a critical review of the research process, including both the systematic review and empirical paper. The strengths and limitations will be discussed, as well as the authors personal development as a scientist practitioner as a result of conducting the research.
A Study of Visualization in Introductory Programming

The teaching of fundamental programming skills is a field that extensively uses different kinds of tools to enhance learning experience. These tools come in several sizes, offering wide range of different equipment or approaches to the teaching of introductory programming curricula. At the same time, computer sciences, and programming courses in particular, suffer from high drop-out rates and falling student grades. Students lose interest on programming because of several complex models and structures have to be learned before anything visually impressive can be created. This problem is intensified by the new multimedia environments like games and applets, whereas command line programs and data algorithms have lost impact and are not considered interesting. So can visualization tools be used to increase the student motivation and create motivational tasks to promote interest towards programming? This paper describes a project to enhance student motivation and interest towards programming in the introductory programming course by applying visualization tool to lecture demonstrations and to the course assignments. We present the results from the course and observed student reactions to
Cross-region traffic prediction for China on OpenStreetMap

OpenStreetMap (OSM) is a free, open-source and popular mapping service. However, due to various reasons, it doesn't offer live traffic information or traffic prediction for China. This paper presents an approach and a system to learn a prediction model from graphical traffic condition data provided by Baidu Map, which is a commercial, close-source map provider in China, and apply the model on OSM so that one can predict the traffic conditions with nearly 90% accuracy in various parts of Shanghai, China, even though no traffic data is available for that area from Baidu Map. This novel system can be useful in urban planning, transportation dispatching as well as personal travel planning.
The Influence of the Personalization and Codification Strategies on Successful Knowledge Management Case Study: National Iranian Oil Company

Successful implementation of Knowledge Management (KM) involves on identification the most effective associated factors. Hence well-thought-out of organization strategy often is absent in implementing KM. Moreover resource restrictions lead us to prioritize them. Consequently, organizations are looking to prioritize the projects for deploying KM in order to invest on the infrastructural projects based on that. The purpose of this study is on identifying, classifying and ranking the enablers related to four key factors of people, process, leadership and information technology based on two given KM strategies. The quantitative approach was utilized in collecting data through questionnaire. In order to approve and classify the identified enablers in each key factor, a quantitative approach was utilized in collecting data through a few justifiable reliable questionnaires. We designed them using Delphi method and distributed among organization experts. Significant factors extracted from applying an exploratory factor analysis using the SPSS package. In order to prioritize the extracted enabler's a fuzzy multi- criteria decision making technique is then applied. The results shows that among thirty identified enablers in personalization strategy, twenty seven ones and among the nineteen identified enablers in codification strategy, sixteen of are significant. Accordingly by ranking the factors and enablers in nine management organizations of the National Iranian Oil Company (NIOC), the leadership factor in personalization strategy and also information technology in codification strategy have higher ranks by weighing 0.259 and 0.267respectively.
Soft Surface Displays : Exploiting Reflection and Dispersion of Liquids

Screen displays based on front and rear projection have become very popular with the broad availability of video projector technology, ranging from professional high-end down to consumer and home entertainment systems. Traditionally, the screen canvas in such settings is made from solid, reflective material like silver cinema screens or white walls – usually in flat, rectangular format. We propose soft material like flowing or vaporized liquids as an alternative to screen hardware, particularly for display systems that inherently gain from the soft nature of liquids, that need to sustain the possibility to trespass the display, that rely on the randomness of the dynamics of liquid and steam dispersion, and that do not allow for bulky, on site physical screen hardware setups. We first study the physics of reflection and refraction for soft material like fog, vapor and crystal liquids, and argue for the particular characteristics of lighting effects that can only be achieved by this material. We assess the utility of controlled and uncontrolled material propagation for the purpose of displays. Finally we report on a real world experiment involving a 200m fountain screen.
Robust and low cost watermarking using image characteristics

Most of the digital image watermarking techniques use pixel values, frequency or other transform coefficients to embed information without considering the perceptually significant portion of the cover. The present work selects the perceptually significant region of the cover and embeds data in the transform coefficients in order to design low-cost robust watermarking scheme. Experimental results using several benchmark image samples are reported.
Mental Rotation of Letters and Shapes in Developmental Dyslexia

Extending the work of Corballis et al (1985, Cortex 21 225–236), we investigated mental rotation of letters (experiment 1), and of letters and shapes (experiment 2) in normal readers and developmental dyslexics. Whereas the overall response times were equal for shapes in both groups, for letters they were slower in dyslexics. For letters as well as for shapes, however, the same mental-rotation effects were obtained between the groups. The results are interpreted as support for the notion of developmental dyslexia as a deficit in functional coordination between graphemic and phonological letter representations.