Journal articles on the topic 'MOPI (Computer system)'

Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index.

An all-diode laser-based master oscillator power amplifier (MOPA) configuration for the generation of ns-pulses with high peak power, stable wavelength and small spectral line width is presented. The MOPA emits alternating at two wavelengths in the spectral range between 964 nm and 968 nm, suitable for the detection of water vapor by absorption spectroscopy. The monolithic master oscillator (MO) consists of two slightly detuned distributed feedback laser branches, whose emission is combined in a Y-coupler. The two emission wavelengths can be adjusted by varying the current or temperature to an absorption line and to a non-absorbing region. The power amplifier (PA) consists of a ridge-waveguide (RW) section and a tapered section, monolithically integrated within one chip. The RW section of the PA acts as an optical gate and converts the continuous wave input beam emitted by the MO into a sequence of short optical pulses, which are subsequently amplified by the tapered section to boost the output power. For a pulse width of 8 ns, a peak power of 16.3 W and a side mode suppression ratio of more than 37 dB are achieved at a repetition rate of 25 kHz. The measured spectral width of 10 pm, i.e., 0.1 cm−1, is limited by the resolution of the optical spectrum analyzer. The generated pulses emitting alternating at two wavelengths can be utilized in a differential absorption light detection and ranging system.

Abstract. The number of GPS trajectories recorded daily has been continuously growing in the recent years and new methods to analyse such big data are surfacing all the time. In this paper, we focus on destination prediction, which is useful in various applications like hazard detection and advertisement. We proposed a real-time method for destination prediction of moving users. It uses the current movement trajectory of the user together with historical and regional information to make an accurate prediction. The method is efficient because we can rapidly compute features with the help of spatial and non-spatial indexing methods. We tested the method with real trajectories collected by Mopsi users. The success rate of the method is up to 65 % depending on the length of the recorded trajectory so far, i.e. how long the user has been on move. To our knowledge, this is the first real-time system capable of such success.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

Purpose: The aims of the study were to identify and evaluate the internal controls used in a computerized accounting system of the SMEs, to identify and evaluate the merits and demerits of the internal controls used in the companies, and to identify the challenges in the implementation of internal control systems in the computerized accounting system of the SMEs. Design/methodology/approach: The population of the study was the employees of the SME’s registered with Ministry of Commerce and Industry (MOCI) in Muscat, Oman. Samples were selected from this population on a random sampling basis. One-member employee from the SMEs representing the enterprise filled the questionnaire. The survey was conducted through a questionnaire using a Google form. 220 samples were collected from the population. The data collected was analyzed using data processing methods and Microsoft excel. Findings: The study found that SMEs were using computers to perform their daily transactions and the accounting software used by SMEs were cheap, of low quality and not proficient enough. One of the components of internal control system, i.e. monitoring was implemented by most of the SMEs and they also stated that they rarely hired an external auditor for auditing because of the expensive auditing fees. It was also stated that the security mechanisms were not properly implemented in the SMEs because of the expensive cost. Research limitations/implications: The study suggested that the Government should emphasize the SMEs to implement a good internal control system. This can reduce the errors and also increase the efficiency of operations. SOX in Omani context should be strictly implemented making it statutory and mandatory for all the SMEs in Oman and such statutory laws will help the auditors to abide by the rules and regulations to be followed. Social Implications : Government should support SMEs through providing education on the importance of internal control systems as most of the employees of SMEs are poorly educated/illiterates. The Government should set a price ceiling to the external auditors on the auditing fees exclusively for SMEs so that they can afford. Originality / Value: No study was carried before in relates to the impact of the internal control system through the computerized accounting system among the SMEs in Muscat, Oman. This study will through light on the statutory laws required for the sake of internal control measures required for the SMEs.

SUMMARY Exhaustive searches in regular grids is a traditional and effective method for inversion, that is numerical solution of systems of non-linear equations which cannot be solved using formal algebraic techniques. However, this technique is effective for very few (3–4) variables and is slow. Recently, the first limitation was to a major degree overpassed with the new TOPological INVersion (TOPINV) algorithm which was used for inversion of systems with up to 18, or even more unknown variables. The novelty of this algorithm is that it is not based on the principle of the mean minimum misfit (cost function) between observations and model predictions, used by most inversion techniques. The new algorithm investigates for each gridpoint whether misfits of each observation are within specified uncertainty intervals, and stores clusters of ‘successful’ gridpoints in matrix form. These clusters (ensembles, sets) of gridpoints are tested whether they satisfy certain criteria and are then used to compute one or more optimal statistical solutions. The new algorithm is efficient for highly non-linear problems with high measurement uncertainties (low signal-to-noise ratio, SNR) and poor distribution of observations, that is problems leading to complicated 3-D mean misfit surfaces without dominant peaks, but it is slow when running in common computers. To overcome this limitation, we used GPUs which permit parallel processing in common computers, but faced another computational problem: GPU parallel processing supports only up to three dimensions. To solve this problem, we used CUDA programming and optimized the distribution of the computational load to all GPU cores. This leads up to 100x speedup relative to common CPU processing, as is derived from comparative tests with synthetic data for two typical inversion geophysical problems with up to 18 unknown variables, Mogi magma source modeling and elastic dislocation modeling of seismic faults. This impressive speedup makes the GPU/CUDA implementation of TOPINV practical even for low-latency solution of certain geophysical problems. This speedup in calculations also permitted to investigate the performance of the new algorithm in relation to the density of the adopted grids. We focused on a typical problem of elastic dislocation in unfavorable conditions (poor observations geometry, data with low SNR) and on synthetic observations with noise, so that the difference of each solution from the ‘true’/reference value was known (accuracy-based approach). Application of the algorithm revealed stable, accurate and precise solutions, with quality increasing with the grid density. Solution defects (bias), mainly produced by very coarse grids, can be identified through specific diagnostic criteria, dictating finer search grids.

In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

This research stems from the casual observation that the image of a television screen with 18° geometric ®eld of view (FOVg) as seen on a real television with 17° real FOV appears much “nearer” than the real television<Eth>as much as 98% closer. Does the image appear “nearer” because the distance to the real television is misjudged or is a mental model of a virtual subjective self-location created? Either way, all the projective mapping in the world, whether only in the z plane or in all x, y, and z planes, is irrelevant to explain this powerful perceptual or cognitive effect that the neglected variable of FOV has on self-location in virtual space. Accurate perception of the scene and precise self-location in virtual environments is the goal of accurate perspective geometries, scene computation, and helmet-mounted display (HMD) optics. Yet, as research has already clearly shown, accurate geometric projection is no guarantee of accurate perception: for instance, images viewed exactly at their proper projection points have repeatedly been seen at distorted distances or inaccurate directions. In addition to precise engineering, it is important to understand the psychology of selflocation, also called egocenters, to obtain an understanding of virtual space. A new theory, Cognitive Frame Theory, is proposed to deal with cognitive modi®cations of perception in a way that emphasizes the importance of self-location. Cognitive Frame Theory builds on the phenomenal geometry underlying self-location perceptions: the localization of objects in space requires a combination of perceived distance, perceived direction, and the perception of the position or motion of the self. A fundamentally novel component of the theory suggests that observers use their natural ®elds of view of 120° vertical by 180° horizontal as the primary standard for interpreting visual displays and their self-location in the space of those displays.

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

Objectives: To assess the evidence of information communication technology (ICT) use in the training of maternal and child health (MCH) workers, discuss methodological issues present in the identified studies, and identify future work areas. Introduction: The explosive growth of cellphone usage in low and middle-income countries (LMIC) has made mobile technology an increasingly attractive form of information communication technology (ICT) to be used to meet healthcare needs that go unmet, rising due to the paucity of trained clinical workers (O’Donovan, Bersin, & O’Donovan, 2015). The portability and relative low cost of cellphones have made them ubiquitous and efficient to use. For example, subscriptions in Africa have risen from 12.4 per hundred inhabitants in 2005 to per hundred inhabitants in 2015 (ITU, 2017). ICT is an umbrella term that encompasses the hardware, software and networks that provide its users with data and information resources. As far as healthcare is concerned, these resources include access to varied tools and services such as electronic health records, point-of-care databases, decision support systems, clinical guidelines or training modules for continuing education (Machingura et al., 2014). This technology has made healthcare more efficient in affluent countries where funding and infrastructure to build, support and maintain ICT is readily available. However, ICT development is critical to LMIC’s which have the greatest barriers to effective and efficient healthcare systems and fewer resources to overcome challenges. The aims of this paper are to (1) summarize the literature on ICT use in the training of MCH workers, (2) discuss methodological issues present in the identified studies, and (3) identify future work areas. Our specific research questions are: Which ICT tools have been used in developing countries for training the MCH workforce? How successful are the tools for instructing health care workers? A major impediment to health care improvements in underdeveloped countries is the low ratio of health professionals to patients. A developed workforce is critical for sustaining healthcare infrastructure. Because there is an insufficient number of professional practitioners, many MCH health needs are met by community workers with limited or no formal training (Chipps et al., 2015). Since the level of services range from general check-ups to life-saving interventions, training must address a variety of educational requirements. (Agarwal et al., 2015). In addition to primary professional education, health workers require training for re-licensure and continuous professional development (CPD). Training, particularly in remote areas, requires travel, time away from work as well as funding for food and lodging (Chipps et al., 2015). This exacerbates uneven healthcare coverage with the majority of MCH health care workers concentrated in urban centers, leaving rural residents with inadequate services (Middleberg et al., 2013; Modi et al., 2015). ICT reduces costs by enabling personnel to remain in their communities while providing digital access to educational content, mentors, guidelines and decision support systems (Saronga et al., 2015). It is commonly recognized that underdeveloped countries have occasional brown-outs in their urban centers and the power grid may not reach rural or remote areas. Even if seed money is acquired for start-up costs, funding for technology maintenance and technical manpower beyond the pilot stage can be tentative (Achampong, 2012). Secondly, while cell phone use across LMICs has exploded in recent years, its use for advancing training has not grown in comparison. A limited number of reports have been published, reporting the use of ICT for communication (Andreatta et al., 2011), tracking health worker behavior (Awoonor-Williams et al., 2013), attitudes towards using ICT (Sukums et al., 2014; Zakane et al., 2014), and the impact of the design of ICT (Valez et. al., 2014). This paucity of studies understanding the impact of ICT on measurable training outcomes leaves a troubling gap in the literature if progress is to be made in addressing the training needs. Finally, government entities, educators and administrators may be reluctant to adopt ICT into health training for practical, fiscal and political reasons. Because health personnel may not have exposure to technology in their daily lives, staff may require basic computer training on operating systems, file management, word processing and databases in conjunction with ICT projects (Sukums, 2014). In addition to a lack of knowledge about computers in general, use of ICT also comes with associated monetary costs. Both of these issues are also exacerbated by resulting government policy changes. We endeavored to fill this gap by completing a literature review to bring the disparate work together, but to our surprise, it did not really exist. This paper reports on (1) what studies have been conducted on the use of ICT in training; (2) what common methods are used and how they are evaluated and (3) what outcomes have been reported. Methods: Medline (OVID), CINAHL and Web of Science were searched for relevant articles published between January 1, 2007 and February 28, 2017. Studies were included if they included training and education in low and middle-income countries using ICT for maternal child health workers. Results: 111 unique articles from electronic searches with seven additional articles discovered through hand-searching reference lists were identified. After review, 15 articles aligned with the necessities to analyze the current environment of the ICT tools. The study designs in the reviewed articles were usually pre- and post-evaluations (n=7). There were also a small number of single cross-sectional studies (n=3) measuring the use of the tool. Two studies also evaluated the use of electronic clinical decision support systems (CDSS) applications or algorithms. The remainder of the studies (n=3) used ICT to provide resources for meeting information needs, as well as repositories of protocols and best practice documents. The outcomes reported ranged from access to medical resources (n=3), accuracy in clinical documentation (n=2), need for remedial computer training (n=2) and an increase in clinical knowledge and proper use of protocols (n=4) Discussion and conclusion: The current evidence-base does not show a clear indication that there were particular initiatives using ICT for the training of health workers. While the majority of projects identified were shown to improve outcomes, there were limited results reported. This lack of documented evidence hinders decisions about the content and methods that should be used to support training. We are missing an opportunity for advancement. The World Health Organization identified community health worker training as a lever to move the improvement of health care in low and middle-income countries (LMICs). An understanding of barriers and facilitators to using ICTs to meet this need, provides key directions for policy makers and non-governmental organizations as they apply limited resources to these issues.

En el Ecuador los problemas de hipoacusia generan en los niños que la padecen graves problemas de comunicación, al ser este un problema de más de 1000 casos al año, se requiere una solución de bajovcosto. Actualmente para solucionar problemas de hipoacusia causados por daños en el oído externo, seusan en casos audífonos de conducción ósea, que son dispositivos que se colocan mediante implante o mediante cintillos unidos a la cabeza, junto al hueso mastoides, este tipo de dispositivos generan rechazo en niños, debido a su incomodidad y evidente notoriedad. En este proyecto se hace uso de la metodología de la Ingeniería Kansei (IK), enfatizando en las emociones para el desarrollo de un dispositivo que cause respuestas positivas en los niños que lo usan y también en sus padres. Para aplicar el método de IK se plantearon 16 diseños de dispositivos de conducción ósea y mediante un análisis estadístico de la teoría de cuantificación se seleccionó un diseño que genere las emociones definidas en los usuarios. El dispositivo diseñado, luego de la evaluación realizada cubre las expectativas de los niños y genera una aceptación por parte del usuario.
 Palabra Claves: Kansei, audífono, conducción ósea, diseño de producto.
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AbstractLarge-scale multi-objective optimization problems (MOPs) that involve a large number of decision variables, have emerged from many real-world applications. While evolutionary algorithms (EAs) have been widely acknowledged as a mainstream method for MOPs, most research progress and successful applications of EAs have been restricted to MOPs with small-scale decision variables. More recently, it has been reported that traditional multi-objective EAs (MOEAs) suffer severe deterioration with the increase of decision variables. As a result, and motivated by the emergence of real-world large-scale MOPs, investigation of MOEAs in this aspect has attracted much more attention in the past decade. This paper reviews the progress of evolutionary computation for large-scale multi-objective optimization from two angles. From the key difficulties of the large-scale MOPs, the scalability analysis is discussed by focusing on the performance of existing MOEAs and the challenges induced by the increase of the number of decision variables. From the perspective of methodology, the large-scale MOEAs are categorized into three classes and introduced respectively: divide and conquer based, dimensionality reduction based and enhanced search-based approaches. Several future research directions are also discussed.

In this paper we consider quantization of complex variables and mean-square error (MSE). The best polar quantizer is Wilson’s unrestricted polar quantizer (UPQ) [1]. The MSE minimization is constrained only by the total number of quantization points, N. Our method is different from Wilson’s algorithm [1] that has predetermined number of points Mi at each magnitude level i, 1?i?L, which makes it impractical for large number of points. In our approach, we consider MSE as a function of the vector M= L i i M ? ? 1 ) (whose elements are numbers of phase quantization levels at each magnitude level. The Wilson's method finds the optimal quantizer in such a way that the decision and reconstruction levels r, m are iterative found for each combination M, while the optimal combination is found by searching all combinations. Wilson's algorithm cannot be applied for middle and great N. The asymptotic analysis of the polar quantizers with circular symmetric densities is given in [2]. This analysis is approximate and cannot be applied for any number of points and for great N, which will be shown in this paper. We define the extension of the MSE over RL (denoted by MSE(P)). We prove the convexity of this function and show an efficient way to find M= L i i M ? ? 1 ) (by Popt. Our algorithm consists of two main iterative processes. The first iterative process finds Popt, ropt, mopt with ? accuracy, while the second iterative process determines Mopt, mopt, ropt using Popt as the starting value. This paper eliminates incompleteness from [1] and [2]. We also give an example of the quantizer construction for a Gaussian source. The authors see their work as a contribution in knowing the best possible solution in these classes of problems and also a possibility of applying the technique exposed inhere on other classes of problems and on larger dimensions.

AbstractIt is a challenging task to identify the objectives on which a certain decision was based, in particular if several, potentially conflicting criteria are equally important and a continuous set of optimal compromise decisions exists. This task can be understood as the inverse problem of multiobjective optimization, where the goal is to find the objective function vector of a given Pareto set. To this end, we present a method to construct the objective function vector of an unconstrained multiobjective optimization problem (MOP) such that the Pareto critical set contains a given set of data points with prescribed KKT multipliers. If such an MOP can not be found, then the method instead produces an MOP whose Pareto critical set is at least close to the data points. The key idea is to consider the objective function vector in the multiobjective KKT conditions as variable and then search for the objectives that minimize the Euclidean norm of the resulting system of equations. By expressing the objectives in a finite-dimensional basis, we transform this problem into a homogeneous, linear system of equations that can be solved efficiently. Potential applications of this approach include the identification of objectives (both from clean and noisy data) and the construction of surrogate models for expensive MOPs.

Abstract In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.