Miracle-dz/UniFAR-FTUs huggingface dataset:
The Application of Multi-objective Genetic K-means Clustering Algorithm to the Analyses of Cardiac Disease Diagnosis

In a case of misdiagnosing of disease , false negativity can cast a much larger impact than false positivity for both the doctor and the patie nt. Rece ntly, many researche rs have de vote d to the study of using data mining on disease diagnosis. Current researc h shows by emphasizing the adoption of various clustering or classification techniques in cardiac disease diagnosis, one fails to lowe r the risk of misjudging a case as false negative . There fore , this research focuses on the combination of multi-objective ge netic algorithm and k-means clustering technique in data mining to analyze the data of patie nts suffe ring from cardiac disease, hoping to ge t higher pre diction accuracy in the diagnosis and lowe r the risk of misjudging a case as false negative , the re sult of which nee ds to pay a high cost. These two algorithms are combine d and name d Multi- obje ctive Ge netic k-means Clustering Algorithm, terme d MOGKCA in this work. The obje ctive s of multi-objective optimization on cardiac disease analysis are : (1) to minimize the inaccuracy of classification (or maximize the pre diction accuracy of classification), and (2) to minimize the number of false negativity (or minimize the cost of classification). The data use d in this research are from UCI cardiac disease data set, He art-staglog. From the single obje ctive optimization case, the propose d MOGKCA can e ffective ly el e vate the classification rate . In the analysis of the two cases of data sets in the ir multi-obje ctive optimization, we can ge t the best Pare to -Front through MOKGCA, which can be provide d and he lp set the best point of analysis.
Modeling and calibrating CCD cameras for illumination-insensitive machine vision

Changes in measured image irradiance have many physical causes and are the primary cue for several visual processes such as edge detection and shape from shading. Using physical models for CCD image sensors and material reflectance, we quantify the variation in digitized pixel values that is due to sensor noise and reflectance variation. This analysis forms the basis of algorithms for sensor characterization and calibration and for scene description. Specifically, algorithms are developed for estimating the parameters of sensor noise and for calibrating a camera to remove the effects of fixed pattern nonuniformity and spatial variation in dark current. While these techniques have many potential uses, we describe in particular how they can be used to estimate a measure of scene variation. This measure is independent of image irradiance and can be used to identify a surface from a single sensor band over a range of situations. Experimental results obtained using these algorithms are presented.
A Selected Descriptor-Indexed Bibliography to the Literature on Artificial Intelligence

This listing is intended as an introduction to the literature on Artificial Intelligence? i.e., to the literature dealing with the problem of making machines behave intelligently. We have divided this area into categories and cross-indexed the references accordingly. Large bibliographies, without some classification facility, are next to useless. This particular field is still young, but there are already many instances in which workers have wasted much time in rediscovering (for better for for worse) schemes already reported. In the last year or two this problem has become worse, and in such a situation just about any information is better than none.
A systematic approach to building the mental health response capacity of practitioners in a post-disaster context

Abstract This paper presents the results of a summative evaluation of the training component of the Australian Government Mental Health Response to the 2009 bushfires in Victoria, Australia. With very little evidence available to date on comprehensive attempts at implementing multilevel training frameworks in the wake of natural disasters, the evaluation provides valuable insights into an effort that sought to build the capacity of practitioners to respond to the psychosocial and mental health consequences of a significant bushfire disaster at various levels of the response system. Key findings of the evaluation are discussed with regards to their relevance for the training of mental health practitioners in a broader range of disaster circumstances.
Hierarchical motion estimation algorithm with smoothness constraints and postprocessing

How to obtain accurate and reliable motion parameters from an image sequence is the key problem in many applications of image processing, image recognition and image coding. Based on the investigation and comparison of the different motion estimation (or optical flow) algorithms existing, a pel-based motion estimation algorithm with smoothness constraints is developed. It is obvious that the performance of a motion estimation algorithm is strongly dependent on the measurement windows which seem to vary with different types of images. In order to further improve accuracy and reliability of motion estimation, a hierarchical motion estimation algorithm is proposed in this paper to cope with different sizes, patterns and complicated motions of objects in scene. This hierarchical estimation algorithm can conveniently be applied for motion-compensated (MC) interframe interpolation coding. Since motion information can be estimated and transmitted as in a block-based forward MC prediction coder, and then the motion vector fields are hierarchically refined to the pel resolution at the receiver to realize a high quality MC interpolation. The experiment results show that the proposed algorithm has an excellent performance, not only very small prediction errors, but also quite smooth and accurate motion estimation.
Smart alarming scheme for ICU using neural networks

In this work a new scheme for intelligent alarming is presented. The idea is that in order for an alarming scheme to be able to be efficient, the definitions of normal, abnormal and intermediate state have to be changed many times on an hour to hour basis, since in ICU the patient state can change dramatically from day to day. In order to do so, unsupervised and supervised learning systems need to be incorporated that can be trained fast and reliably by the medical personnel. Thus the need for a system that can be trained fast and the existence of a user-friendly MMI where the doctor shall be able to modulate the boundaries between normal, abnormal and intermediate values according to the patient's condition is imperative. In this paper, this approach is implemented, using neural networks (NN) for training and learning, and a user friendly MMI using colours and 2-D phase planes of parameters monitored in ICU are used to achieve more efficient alarming schemes.
A Hash Algorithm for IP Flow Measurement

In order to solve the problems with computing resource and high-speed network traffic, it is necessary to deal with the network traffic by some measuring technologies, such as sampling measurement and load balance, etc, while the hash algorithm is one of the key measuring technologies. In this paper, firstly, a random metric is provided to evaluate the performance of the hash algorithms. Secondly, the randomicity of XOR and shift operations are analyzed, and it is proved that the two operations can improve the bit randomicity. Thirdly, this paper analyzes the four fields of IP packet, such as source IP, destination IP, source port, and destination port, and a hash algorithm named XOR_SHIFT is provided based on the analysis. Finally, using the CERNET backbone traffic and PMA traffic, this paper analyzes the character of the XOR_SHIFT hash algorithm and compares with the performance among XOR_SHIFT, IPSX and CRC32 hash algorithms. This study shows that the XOR_SHIFT hash function provided in this paper has two advantages: algorithm performance and hash randomicity, and it can be applied to measure the high-speed network traffic.
Combination antiretroviral strategies for the treatment of pregnant HIV-1-infected women and prevention of perinatal HIV-1 transmission.

Context The Women and Infants Transmission Study is a prospective natural history study that has been enrolling HIV-1-infected pregnant women and their infants since 1989. Objective To evaluate the impact of different antiretroviral regimens on perinatal HIV-1 transmission at the population level. Design Prospective cohort study. Plasma HIV-1 RNA levels were serially measured in 1542 HIV-1-infected women with singleton live births between January 1990 and June 2000. Main outcome measure HIV-1 status of the infant. Results HIV-1 transmission was 20.0% (95% confidence interval [CI], 16.1%-23.9%) for 396 women who not receiving prenatal antiretroviral therapy; 10.4% (95% CI, 8.2%-12.6%) for 710 receiving zidovudine monotherapy; 3.8% (95% CI, 1.1%-6.5%) for 186 receiving dual antiretroviral therapy with no or one highly active drug (Multi-ART); and 1.2% (95% CI, 0-2.5%) for 250 receiving highly active antiretroviral therapy (HAART). Transmission also varied by maternal delivery HIV RNA level: 1.0% for 30,000 copies/mL (p =.0001 for trend). The odds of transmission increased 2.4-fold (95% CI, 1.7-3.5) for every log10 increase in delivery viral load. In multivariate analyses adjusting for maternal viral load, duration of therapy, and other factors, the odds ratio for transmission for women receiving Multi-ART and HAART compared with those receiving ZDV monotherapy was 0.30 (95% CI, 0.09-1.02) and 0.27 (95% CI, 0.08-0.94), respectively. Conclusion Levels of HIV-1 RNA at delivery and prenatal antiretroviral therapy were independently associated with transmission. The protective effect of therapy increased with the complexity and duration of the regimen. HAART was associated with the lowest rates of transmission.
A Systematic Study of Design Conflict: Modeling, Representation, Resolution, and Application

ABSTRACT A Systematic Study of Design Conflict: Modeling, Representation, Resolution, and Application Baiquan Yan, Ph.D. Concordia University, 2013 Conflicts drive the development of technical systems and the evolution of design process. Conflict management, which mainly includes conflict identification and resolution, is a crucial part of design activity. This research conducts a systematic study and proposes a formal structure of design conflicts. The first step of conflict management is to build up a formal model for technical system. Currently, there exist some inconsistences among different design theories because of the lack of a cohesive set of fundamental concepts about technical systems. This lack also causes misunderstanding among researchers and therefore hinders the development of design theories. This thesis presents a formal approach to representing technical systems. Both theoretical derivation and extensive example have shown that this formal representation meets the five requirements: completeness, clarity, independence, flexibility, and adaptability. A set five concepts— purpose, function, structure, behaviour and state— is identified and formally defined as the base set for technical systems. The second step is to model conflicts based on the formalization of technical system. Current studies are based on heuristics and lack a systematic approach, and therefore fail to detect conflicts that are not predefined. This research puts forward a formal structure of design conflicts based on systematic analysis. This formal structure shows that any conflict is composed of at least three objects: two competing objects and one resource object that the former two contend for. This formal structure can be applied to different design fields and helps designers identify all conflicts existed in different design stages. Based on the formal structure of conflicts and analysis of relation among the three objects in a conflict, this research also proposes three formal methods for detecting conflicts and presents a set of general resolution principles, which include modifying resource object, separating conflict relations in time or in space, changing the two competing objects, using optimization methods, and replacing the whole conflict. An example demonstrates the application of the formal structures, followed with conclusion and suggestions for future research.
Efficient Method to Support Mobile Virtualization-based Cloud Resource Management

Abstract Recently, various cloud service has been being provided on mobile devices as well as desktop pc and server computer. Also, Smartphone users are very rapidly increasing, and they are using it for enjoying various services(cloud service, game, banking service, mobile office, etc.). So, research to utilize resources on mobile device has been conducted. In this paper, We have suggested efficient method of cloud resource management by using information of available physical resources(CPU, memory, storage, etc.) between mobile devices, and information of physical resource in mobile device. Suggested technology is possible to guarantee real-time process and efficiently manage resources. Key Words : Mobile Virtualization, Mobile Cloud Computing, Cloud Resource Management Received 2 January 2014, Revised 7 February 2014Accepted 20 February 2014Corresponding Author: Jooman Kim(Pusan National University) Email: joomkim@pusan.ac. krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.otg/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN:1738-1916 properly cited.
Pueden ocho semanas de ejercicio físico combinado normalizar marcadores metabólicos de sujetos hiperglicémicos y dislipidémicos

PROPOSITO: Metodologias de actividad fisica de corta duracion han probado un rol en la mejora de alteraciones metabolicas como insulino resistencia (IR) e hiperglicemia. OBJETIVO: Investigar los efectos de un programa de 8 semanas de ejercicio fisico combinado en los niveles de IR y lipidos plasmaticos de mujeres sanas, con hiperglicemia e hipercolesterolemia. Un objetivo adicional fue analizar los efectos de la intervencion en la composicion corporal, presion arterial y rendimiento fisico aerobico. METODOS: Nosotros estudiamos 38 sujetos con alteracion metabolica, asignados a uno de cuatro grupos; hiper-glicemicos (HG, n= 9), hiper-colesterolemicos (HC, n= 10), hiper-glicemicos/hiper-colesterolemicos (HG/HC, n= 9) y sujetos sanos (SANOS, n= 10), quienes realizaron un programa corto de ejercicio fisico combinado durante 8 semanas. RESULTADOS: La glicemia disminuyo significativamente (P<0,01) en HG -12% e HG/HC -14%. La insulina se redujo significativamente (P<0,05) en todos los grupos en rango -26,5 a 37%. El HOMAIR disminuyo en todos los grupos en rango -29 a 40%. No se registraron cambios significativos en el C-LDL en ninguno de los grupos. CONCLUSIONES: Ocho semanas de ejercicio fisico combinado disminuyen los niveles de insulino resistencia, y mejoran el rendimiento fisico aerobico de mujeres sanas y con hiperglicemia e hipercolesterolemia.
A Novel Reliability-Driven Heuristic for Grid Task Scheduling

Under dynamic, unreliable grid environment, a novel heuristic based on average reliability of task is proposed, which task into account the failure condition and dynamic workload of Grid. On the basis of the heuristic strategy proposed, this paper improved two classic scheduling algorithms, Min-min and Sufferage, to improve execution reliability of grid task. Moreover, a new greedy algorithm, GR, is proposed in the paper. The simulation experimental results indicate that the algorithms based on the average reliability heuristic are better than Min-min and Sufferage in terms of task completion ratio by 10%, which can guarantee the user's deadline efficiently.
Automatic music genres classification as a pattern recognition problem

Music genres are the simplest and effect descriptors for searching music libraries stores or catalogues. The paper compares the results of two automatic music genres classification systems implemented by using two different yet simple classifiers (K-Nearest Neighbor and Naïve Bayes). First a 10-12 second sample is selected and features are extracted from it, and then based on those features results of both classifiers are represented in the form of accuracy table and confusion matrix. An experiment carried out on test 60 taken from middle of a song represents the true essence of its genre as compared to the samples taken from beginning and ending of a song. The novel techniques have achieved an accuracy of 91% and 78% by using Naïve Bayes and KNN classifiers respectively.
DSP-based algorithm for electric power measurement

The paper describes a new approach to the design of digital algorithms for electric power measurements under nonsinusoidal conditions. Using the discrete Fourier transform of two sets of real data in one pass, the voltage and current spectra are obtained, simultaneously. Real and imaginary parts of both spectra are then processed to obtain the terms of the electric power components according to various definitions in both the time domain and the frequency domain. Some results of simulated measurements using an assembler program for the TMS 32010 micro-computer are given.
Implementing Technology in the Foreign Language Curriculum: Redefining the Boundaries between Language and Culture

This article reports a case study of the implementation of an innovative set of computer programs specifically designed to accompany a college level 1 French text. The computer materials were culturally based and included not only Parisian French speakers but also speakers from francophone countries in Africa and the Caribbean. The students' reactions were mixed. They complained that the culture lessons were unimportant and distracted from their learning the grammar rules and vocabulary. The study indicated that understanding college level students' beliefs about language learning is helpful if innovative language programs are to be successful.
[Results of bariatric surgery. Experience over 18 years].

BACKGROUND Bariatric surgery is the gold-standard treatment for morbid obesity because it has low morbidity rates in high-volume centers and generates long term sustained weight loss. AIM To describe our experience in bariatric surgery since the creation of our bariatric program in 1992. MATERIAL AND METHODS Retrospective analysis of all patients subjected to bariatric surgery from 1992 to December 2010. Data was obtained from the electronic institutional registry. The Procedures per-formed were open and laparoscopic Roux-en-Y gastric bypass (BPGA and BPGL, respectively), laparoscopic adjustable gastric band (BGAL) and laparoscopic sleeve gastrectomy (GML). RESULTS A total of 4943 procedures were performed, 768 (16%) BPGA, 2558 (52%) BPGL, 199 (4%) BGAL and 1418 (29%) GML. The number of procedures progressively increased, from 100 cases in 2000 to over 700 cases in 2008. Proportion of femóles and preoperative mean body mass Índex fluctuated between 69 and 79% and 35 and 43 kg/m², respectively, among the different procedures. Early and late complications fluctuated between Oto 1% (higher on BPGA) and 3 to 32.7% (higher on BGAL), respectively. The excess weight lost at five years was 76.1% in BPGA, 92.5% in BPGL and 53.7% in BGAL. The figure for GML at three years was 73.7%. CONCLUSIONS The complication rates of this series of patients are similar to those reported in large series abroad. BPGL is still the most effective procedure; however GML is an attractive alternative for less obese patients.
SOCIAL CLASSIFICATION AND FOLKSONOMY IN ART MUSEUMS: EARLY DATA FROM THE STEVE.MUSEUM TAGGER PROTOTYPE

The collections of art museums have been assembled over hundreds of years and described, organized and classified according to traditions of art historical research and discourse. Art museums, in their role as curators and interpreters of the cultural record, have developed standards for the description of works of art (such as the Categories for the Description of Works of Art, CDWA) that emphasize the physical nature of art as artefact, the authorial role of the creator, the temporal and cultural context of creation and ownership, and the scholarly significance of the work over time. Collections managers have recorded conservation, exhibition, loan and publication history, along with significant volumes of internal documentation of acquisition and storage, that support the custody and care of artefacts of significant cultural value. But the systems of documentation and classification that support the professional discourse of art history and the management of museum collections have failed to represent the interests, perspectives or passions of those who visit [use? ] museum collections, both on-site and online. As museums move to reflect the breadth of their audiences and the diversity of their perspectives, so must museum documentation change to reflect concerns other than the traditionally art historical and museological. Social tagging offers a direct way for museums to learn what museum-goers see in works of art, what they judge as significant and where they find or make meaning. Within the steve collaboration(http://www.steve.museum), a group of art museums is collectively exploring the role of social tagging and studying the resulting folksonomy (Bearman and Trant, 2005; Chun, Cherry, Hiwiller, Trant, and Wyman, 2006; Trant and Wyman, 2006). Analysis of terms collected in the prototype steve tagger suggests that social tagging of art museum objects can in fact augment museum documentation with unique access points not found in traditional cataloguing. Terms collected through social tagging tools are being compared to museum documentation, to establish the actual contributions made by naive users to the accessibility of art museum collections and to see if social classification provides a way to bridge the semantic gap between art historians and art museums’ publics.
Learning Facial Expression Codes with Sparse Auto-Encoder

A sparse auto-encoder model was trained to extract the code of different facial expression, which comprises four encoder layers and three decode layers, the representation locating in the fourth layer (code layer) is the features expected. With large amounts of patches randomly selected from training faces, the model was trained firstly via backpropagation which minimizes an unsupervised sparse reconstruction error, and then a softmax classifier was learned for supervised classification. The input vector for the classification is the feature of facial image induced by the learned sparse auto-encoder and two key operations (convolving and pooling). Using a small number of hidden units per layer and a relatively small number of training set, the proposed model achieves excellent performance in the experiments.