Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary fuzzy mixture models: applications in anaesthesia

This paper presents a novel application of an Evolutionary Programming based Fuzzy Mixture Model (FMM) system for the determination of a patient’s suitability for surgical anaesthesia. Over a three month period 150 patients were examined where blood pressure, heart rate, and arterial oxygenation data was taken, alongside clinical history. Each patient was assessed and their suitability for surgical general anaesthesia (GA) was determined by a consultant anaesthetist and categorised into 3 groups: ‘suitable for GA’, ‘clearly unsuitable for GA’, ‘possibly suitable for GA (referral.)’. In this work, the FMM rules and mixture membership function parameters for a fuzzy classifier were evolved to produce a discrimination system for the above problem. The evolved system was validated against the classifications made by the consultant and compared to benchmark discrimination systems, namely the Multi-Layer Perceptron (MLP) and Radial Basis Function Network (RBFN). In our initial experiments the evolutionary FMM generated a series of mixture membership functions and rule-bases which successfully classified 79.53% of the medical dataset. It over-performed the benchmark methods, as the MLP could not successfully learn the discrimination hyperplanes, and the RBFN attained a lower correct classification rate. The Evolutionary FMM was shown to be an attractive mechanism for the computationally-lightweight and robust analysis of this kind of data.
E-Social Constructivism and Collaborative E-Learning

Social constructivism is an established educational theory based on the principle that learners and teachers co-construct knowledge through social processes. This chapter proposes an updated theory, e-social constructivism, that takes into account the milieu of electronic communications in which e-learning occurs. Thinkers such as Dewey, Piaget, Vygotsky, and Bruner, who laid the theoretical foundations of social constructivism, wrote in a time when face-to-face interactions were the basis for instruction. The works of these writers are reviewed in this chapter. Together with the results of the author’s phenomenological study of collaborative e-learning, they form the basis of e-social constructivist theory. The author uses grounded theory and situational analysis to derive and support e-social constructivist theory. This chapter discusses the implication of that theory for research, teaching and instructional design.
Pricing-Based Resource Allocation in Virtualized Cloud Radio Access Networks

With centralized processing, cooperative radio, and cloud infrastructure, cloud radio access network (CRAN) has attracted much attention due to its flexibility in network operation and resource management. In this paper, we propose a pricing-based resource allocation strategy in virtualized CRANs, where one mobile network operator (MNO) owns the physical resource and multiple mobile virtual network operators (MVNOs) serve their users with resources leased from the MNO. The problem is naturally formulated as a bi-level optimization problem, where the upper level corresponds to revenue maximization of the MNO and the lower level corresponds to the utility-surplus maximization of the MVNOs. To solve the problem efficiently, we propose to solve the MVNO problem with a low-complexity algorithm, which scales well with the network size. Based on the analysis of the revenue of the MNO, we find that different utility function families result in distinct revenue trends with respect to the price, and correspondingly propose efficient algorithms to find the optimal price that maximizes the revenue. Through simulations, we demonstrate that the proposed algorithm for the MVNO problem can reduce the complexity from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(M^3)$</tex-math></inline-formula> of interior point methods to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(M)$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> is the number of users. Meanwhile, the simulation results show that the proposed pricing methods can find the optimal price in a few iterations.
Multi-Objective Particle Swarm Optimization using Generalized Data Envelopment Analysis

Several meta-heuristic methods such as genetic algorithms and particle swarm optimization (PSO) have been applied for solving multi-objective optimization problems, and have been observed to be useful for generating the whole Pareto optimal solutions. In this research, we propose a new method of multi-objective particle swarm optimization by using generalized data envelopment analysis (GDEA) in order to improve the convergence and the diversity when searching for the solutions as well as to decide easily parameters in PSO. In addition, we investigate the effectiveness of the proposed PSO method using GDEA through some numerical examples.
Electromagnetic-like mechanism for job-shop scheduling by novel heuristic initialization

This paper describes a novel method to enhance the performance of population-based algorithms in solving the job-shop scheduling problem. A novel heuristic initialization technique that is based on the concept of head and tail paths is applied to produce a new initial population. The proposed method is based on an intelligent skip from the primal point of the solution space to a better one, which is achieved by shortening the maximum head and tail paths (SMHT) of all jobs on the given machine. Also in this method, the electromagnetic-like mechanism is applied as an improvement algorithm as it is the state-of-the-art choice to improve the produced initial populations. The experimental results show that the quality of the initial population produced by SMHT is better than that produced by some state-of-the-art techniques. Moreover, the experimental results for the Electromagnetic-like Mechanism part of the method show that SMHT makes a significant contribution to accelerating the convergence speed of the improvement algorithm to optimality and improves the obtained output results.
An Embedded System Design to Build Real-Time 2D Maps for Unknown Indoor Environments

In this paper, a remotely controllable, differentially driven wheeled mobile robot was developed in order to build 2D maps of unknown indoor environments; this system would eliminate the need to pre-explore such environments. Main aim of the study is to develop a system with high accuracy by using minimum number of sensors and a processor with low cost especially for comparatively small indoor areas. The distance traveled was calculated using the wheel odometry method. Obstacles surrounding the robot, the distance traveled, and the robot’s orientation were obtained using an ultrasonic distance sensor, optical encoder, and a 3D orientation sensor (also known as an Attitude and Heading Reference System –AHRS), respectively. In addition, the characteristics of the system hardware components were empirically explored, and the errors resulting from the sensors were evaluated. The non-linearity percentage error arising from the encoder was defined and then compensated for. The hysteresis behavior of the ultrasonic distance sensors was also empirically tested. All of the tasks were conducted by using a low-cost FPGA (Field Programmable Gate Arrays) board. This study used the graphical development platform, National Instruments (NI) LabVIEW, and it’s FPGA Module which is used for programming of embedded systems instead of the text-based HDLs (Hardware Description Languages). This distinguishes the proposed system from similar prior studies.
Image registration for limited-view photoacoustic imaging using two linear array transducers

Imaging complicated structures with photoacoustic (PA) modality when the field of view is limited can result in significant imaging artifacts or missing structures. Approaches to solve this problem include new reconstruction algorithms and specific transducer structures, such as hemi-spherical transducer arrays for breast cancer detection. However, most existing PA imaging techniques require either fullview complete projection data collection or complex and computationally-intensive reconstructions. Such approaches are not only time-consuming but also unsuitable for many clinical applications, because most clinical imaging hardware is constrained to limited reconstruction angles. In this paper, we present a method of using two commercial linear array transducers at different orientations to increase the view angle and thus improve the reconstruction of PA imaging. The method involves a two-step process. First, a calibration phantom is imaged to calibrate the relative position of these two linear transducers. Second, two PA images are obtained by a simple back projection algorithm and these images are registered using the information from the calibration process. The final registered image contains more detailed structures without the requirements of a specialized transducer or long processing time. Experimental results show that this method has the potential to provide good image quality using standard low-cost transducers.
Summarizing Spoken Documents Through Utterance Selection

The inherently linear and sequential property of speech raises the need for ways to better navigate through spoken documents. The strategy of navigation I focus on in this thesis is summarization, which aims to identify important excerpts in spoken documents. A basic characteristic that distinguishes speech summarization from traditional text summarization is the availability and utilization of speech-related features. Most previous research, however, has addressed this source from the perspective of descriptive linguistics, in considering only such prosodic features that appear in that literature. The experiments in this dissertation suggest that incorporating prosody does help but its usefulness is very limited—much less than has been suggested in some previous research. We reassess the role of prosodic features vs. features arising from speech recognition transcripts, as well as baseline selection in error-prone and disfluency-filled spontaneous speech. These problems interact with each other, and isolated observations have hampered a comprehensive understanding to date. The effectiveness of these prosodic features is largely confined because of their difficulty in predicting content relevance and redundancy. Nevertheless, untranscribed audio does contain more information than just prosody. This dissertation shows that collecting statistics from far more complex acoustic patterns does allow for estimating state-of-the-art summarization models directly. To this end, we propose an acoustics-based summarization model that is estimated directly on acoustic patterns. We empirically determine the extent to which this acoustics-based model can effectively replace ASR-based models. The extent to which written sources can benefit speech summarization has also been limited, namely to noisy speech recognition transcripts. Predicting the salience of utterances can indeed benefit from more sources than raw audio only. Since speaking and writing are two basic ways of communication and are by nature closely related to each other, in many situations, speech is accompanied with relevant written text. Richer semantics conveyed in the relevant written text provides additional information over speech by itself. This thesis utilizes such information in content selection to help identify salient utterances in the corresponding speech documents. We also employ such richer content to find the structure of spoken documents—i.e., subtopic boundaries—which may in turn help summarization.
A unified approach for hierarchical imaging based on joint hypothesis testing and parameter estimation

The authors present a single formulation for constrained imaging that fuses the problem of joint estimation of the continuous parameters using MAP (maximum a posteriori) and conditional-mean estimators with that of performing generalized Bayes hypothesis testing for the symbolic imaging variables. Coupling this with recent results on representing regular grammars via Gibbs' distributions makes it possible to incorporate into a single hierarchical framework the stochastic constraints relevant to continuous-valued parameters as well as language-theoretic constraints on the symbolic variables. The authors also present a method for performing the required computations on a massively parallel architecture, which makes it possible to update every variable at every level in the hierarchy in parallel. The conclusions obtained are supported with results for a Poisson imaging problem computed on a DAP-500 massively parallel processor with 1024 processing elements. <<ETX>>
Subdivision Methods for Geometric Design: A Constructive Approach

From the Publisher: The world's leading animation houses rely increasingly on subdivision methods for creating realistic-looking complex shapes. However, until now there was no one book devoted to this powerful geometric modeling technique. Subdivision Methods for Geometric Design does the job with authority and precision, providing all that is needed to understand how subdivision works its magic, and how to make that magic work. Throughout the book, icons cue readers to visit a companion Web site loaded with interactive exercises, implementations of the book's images, and supplementary material. Rich in theory, analysis, and practical information, this book is the complete resource for subdivision methods. Features The result of a collaboration between a leading university researcher and an industry practitioner. The only book devoted exclusively and comprehensively to this important new technology. Provides solid background and theoretical analysis of subdivision as well as a wide variety of specific applications. Addresses algorithms for Bezier and uniform B-Spline curves, Catmull-Clark subdivision for quad meshes, and regularity tests for polyhedral meshes. Via the companion Web site, (www.subdivision.com), provides opportunities for readers to experiment hands-on with implementations in a richly interactive environment. Includes a foreword by Tony DeRose, recipient of the 1999 ACM Computer Graphics Achievement Award for his seminal work in subdivision methods. Author Biography: Joe Warren, Professor of Computer Science at Rice University since 1986, is one of the world's leading experts on subdivision. Of his nearly 50 computer science papers-published in prestigious forums such as SIGGRAPH, Transactions on Graphics, Computer-Aided Geometric Design, and The Visual Computer-a dozen specifically address subdivision and its applications to computer graphics. Prof. Warren received both his M.S. and Ph.D. in Computer Science at Cornell University. His research interests focus on mathematical methods for representing geometric shape. Henrik Weimer is a research scientist at the DaimlerChrysler Corporate Research Center in Berlin, where he works on knowledge-based support for the design and creation of engineering products. Dr. Weimer obtained his Ph.D. in Computer Science from Rice University.
Learning User Preference from Heterogeneous Information for Store-Type Recommendation

Online stores are capable analyzing user preference from click logs and transaction records, while retailers of physical stores still lack effective methods to understand user preference. Traditional ways are predominantly field surveys, which are not effective as they need labor-intensive survey thus limit to small populations. As mobile devices and social media are becoming more and more pervasive, user-generated heterogeneous information (e.g., check-in activities and textual reviews) from these platforms are providing rich information to in-depth understand user preference. In this paper, we present a recommendation model for physical stores by learning user's preference from user-generated heterogeneous information. Specifically, the proposed model consists of two phases: 1) offline modeling multi-relation among users, stores and aspects; 2) online graph-based recommendation. The offline modeling phase is designed to learn two kinds of relations: <italic>User-Store</italic> relation and Store-Aspect relation, while the online recommendation phase automatically produces top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="chen-ieq1-2755009.gif"/></alternatives></inline-formula> recommended stores based on the learnt relations with a graph-based model. To demonstrate the utility of our proposed model, we have performed a comprehensive experimental evaluation on two real-world datasets, which are collected by more than 120,000 users during 12 months. Experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.
Wait and you shall see: sexual delay discounting in hypersexual Parkinson’s disease

Patients with Parkinson's disease may develop impulse control disorders under dopaminergic treatments. Impulse control disorders include a wide spectrum of behaviours, such as hypersexuality, pathological gambling or compulsive shopping. Yet, the neural systems engaged in specific impulse control disorders remain poorly characterized. Here, using model-based functional MRI, we aimed to determine the brain systems involved during delay-discounting of erotic rewards in hypersexual patients with Parkinson's disease (PD+HS), patients with Parkinson's disease without hypersexuality (PD - HS) and controls. Patients with Parkinson's disease were evaluated ON and OFF levodopa (counterbalanced). Participants had to decide between two options: (i) wait for 1.5 s to briefly view an erotic image; or (ii) wait longer to see the erotic image for a longer period of time. At the time of decision-making, we investigated which brain regions were engaged with the subjective valuation of the delayed erotic reward. At the time of the rewarded outcome, we searched for the brain regions responding more robustly after waiting longer to view the erotic image. PD+HS patients showed reduced discounting of erotic delayed rewards, compared to both patients with Parkinson's disease and controls, suggesting that they accepted waiting longer to view erotic images for a longer period of time. Thus, when using erotic stimuli that motivate PD+HS, these patients were less impulsive for the immediate reward. At the brain system level, this effect was paralleled by the fact that PD+HS, as compared to controls and PD - HS, showed a negative correlation between subjective value of the delayed reward and activity of medial prefrontal cortex and ventral striatum. Consistent with the incentive salience hypothesis combining learned cue-reward associations with current relevant physiological state, dopaminergic treatment in PD+HS boosted excessive 'wanting' of rewards and heightened activity in the anterior medial prefrontal cortex and the posterior cingulate cortex, as reflected by higher correlation with subjective value of the option associated to the delayed reward when ON medication as compared to the OFF medication state. At the time of outcome, the anterior medial prefrontal/rostral anterior cingulate cortex showed an interaction between group (PD+HS versus PD - HS) and medication (ON versus OFF), suggesting that dopaminergic treatment boosted activity of this brain region in PD+HS when viewing erotic images after waiting for longer periods of time. Our findings point to reduced delay discounting of erotic rewards in PD+HS, both at the behavioural and brain system levels, and abnormal reinforcing effect of levodopa when PD+HS patients are confronted with erotic stimuli.10. 1093/brain/awy298_video1awy298media15983845074001.
Sci‐Thur PM – Brachytherapy 02: Positional accuracy in Pd‐103 permanent breast seed implant (PBSI) brachytherapy at the Tom Baker Cancer Centre (TBCC)

Purpose: This retrospective study aims to quantify the positional accuracy of seed delivery in permanent breast seed implant (PBSI) brachytherapy at the Tom Baker Cancer Centre (TBCC). Methods: Treatment planning and post-implant CT scans for 5 patients were rigidly registered using the MIM Symphony™ software (MIM Software, Cleveland, OH) and used to evaluate differences between planned and implanted seed positions. Total and directional seed displacements were calculated for each patient in a clinically relevant ‘needle coordinate system’, defined relative to the angle of fiducial needle insertion. Results: The overall average total seed displacement was 10±8 mm. Systematic seed displacements were observed in individual patients and the magnitude and direction of these offsets varied among patients. One patient showed a significant directional seed displacement in the shallow-deep direction compared with the other four patients. With the exception of this one patient outlier, no significant systematic directional displacements in the needle coordinate system were observed for this cohort; the average directional displacements were −1±5 mm, 2±3 mm, and −2±4 mm in the shallow-deep, up-down, and right-left directions respectively. Conclusion: With the exception of one patient outlier, the magnitude of seed displacements were relatively consistent among patients. The results indicate that the shallow-deep direction possesses the largest uncertainty for the seed delivery method used at the TBCC. The relatively large uncertainty in seed placement in this direction is expected, as this is the direction of needle insertion. Further work will involve evaluating deflections of delivered needle tracks from their planned positions.
Steganography on multiple MP3 files using spread spectrum and Shamir's secret sharing

The purpose of steganography is how to hide data into another media. In order to increase security of data, steganography technique is often combined with cryptography. The weakness of this combination technique is the data was centralized. Therefore, a steganography technique is develop by using combination of spread spectrum and secret sharing technique. In steganography with secret sharing, shares of data is created and hidden in several medium. Medium used to concealed shares were MP3 files. Hiding technique used was Spread Spectrum. Secret sharing scheme used was Shamir's Secret Sharing. The result showed that steganography with spread spectrum combined with Shamir's Secret Share using MP3 files as medium produce a technique that could hid data into several cover. To extract and reconstruct the data hidden in stego object, it is needed the amount of stego object which more or equal to its threshold. Furthermore, stego objects were imperceptible and robust.