Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.
Many nonlinear problems that arise in various science and engineering fields can be
modelled by the Goursat partial differential equations. Modelling these non-linear
problems using the Goursat partial differential equations has not received much
attention especially the theoretical aspect . The proposed scheme of solution is
supported by examining a nonlinear Goursat problem. The verification of the
theoretical results from several series of numerical experiments are discussed. Results
obtained from Taylor series expansion show that the proposed new scheme is
consistent. By using the von Neumann analysis and essence of stability, the proposed
new scheme is found to be unconditionally stable. In addition, the trend of the
numerical results shows that the new scheme is also convergent.
After publication of the article , it has been brought to our attention that two of the labels on Figure 4 have transposed. The labels "S-type SSU rRNA" and "A-type SSU rRNA" should be in opposite places.
Thousands of prokaryotic genera have been published, but methodological bias in the study of prokaryotes is noted. Prokaryotes that are relatively easy to isolate have been well-studied from multiple aspects. Massive quantities of experimental findings and knowledge generated from the well-known prokaryotic strains are inundating scientific publications. However, researchers may neglect or pay little attention to the uncommon prokaryotes and hard-to-cultivate microorganisms. In this review, we provide a systematic update on the discovery of underexplored culturable and unculturable prokaryotes and discuss the insights accumulated from various research efforts. Examining these neglected prokaryotes may elucidate their novelties and functions and pave the way for their industrial applications. In addition, we hope that this review will prompt the scientific community to reconsider these untapped pragmatic resources.
This report presents a review of the Malaysian Journal of Medical Sciences' (MJMS) performance status throughout 2017, which covers the submission pattern of original manuscripts by month, the geographical distribution of submitting authors, the types of manuscripts and overall acceptance/rejection rates. As the years progress, MJMS continues to receive an escalating number of manuscript submissions. This contributes to an ever-increasing workload, which makes administrative tasks continually more challenging. Although the manuscript submission platform seeks to minimise the pre-publication workload of the journal administrator, it is still a time-consuming task, particularly when authors seek exclusive attention for their submitted manuscripts.
Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic computing whereby a single logic gate can perform the arithmetic operation by exploiting the nature of probability math. SC was proposed in the 1960s when binary computing was expensive. However, presently, SC started to regain interest after the widespread of deep learning application, specifically the convolutional neural network (CNN) algorithm due to its practicality in hardware implementation. Although not all computing functions can translate to the SC domain, several useful function blocks related to the CNN algorithm had been proposed and tested by researchers. An evolution of CNN, namely, binarised neural network, had also gained attention in the edge computing due to its compactness and computing efficiency. This study reviews various SC CNN hardware implementation methodologies. Firstly, we review the fundamental concepts of SC and the circuit structure and then compare the advantages and disadvantages amongst different SC methods. Finally, we conclude the overview of SC in CNN and make suggestions for widespread implementation.
A boy with attention deficit and hyperactivity disorder (ADHD) presented with a fetish for and the subsequent stealing of female undergarments. He was predominantly inattentive and had been a slow learner. Psychological tests showed that he had significant cognitive and inattention problems without significant hyperactivity, and was at risk of dyslexia as well as conduct disorder.
Matched MeSH terms: Attention Deficit Disorder with Hyperactivity/complications*; Attention Deficit Disorder with Hyperactivity/psychology
The purpose of this study was to examine the measurement (configural, metric, scalar, and residual) and structural (factor variance, factor covariance, and factor means) invariance of parent ratings of the attention-deficit/hyperactivity disorder - inattention (ADHD-IN), ADHD - hyperactivity/impulsivity (ADHD-HI), and oppositional defiant disorder (ODD) symptoms as described in the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; American Psychiatric Association, 1994) across boys and girls. In an American pediatric sample (N = 1,015) and a Malaysian elementary school-age sample (N = 928), there was strong support for configural, metric, scalar, residual, factor variance, and covariance invariance across gender within each sample. Both American and Malaysian boys had significantly higher scores on the ADHD-IN and ADHD-HI factor means than did girls, whereas only in the American sample did boys score significantly higher on the ODD factor than did girls. The implications of the results for the study of gender, ethnic, and cultural differences associated with ADHD and ODD are discussed.
Matched MeSH terms: Attention Deficit Disorder with Hyperactivity/epidemiology*; Attention Deficit Disorder with Hyperactivity/psychology*; Attention Deficit and Disruptive Behavior Disorders/epidemiology*; Attention Deficit and Disruptive Behavior Disorders/psychology*
Memory performance is usually impaired when participants have to encode information while performing a concurrent task. Recent studies using recall tasks have found that emotional items are more resistant to such cognitive depletion effects than non-emotional items. However, when recognition tasks are used, the same effect is more elusive as recent recognition studies have obtained contradictory results. In two experiments, we provide evidence that negative emotional content can reliably reduce the effects of cognitive depletion on recognition memory only if stimuli with high levels of emotional intensity are used. In particular, we found that recognition performance for realistic pictures was impaired by a secondary 3-back working memory task during encoding if stimuli were emotionally neutral or had moderate levels of negative emotionality. In contrast, when negative pictures with high levels of emotional intensity were used, the detrimental effects of the secondary task were significantly attenuated.
Confirmatory factor analysis (CFA) was used to model a multitrait by multisource matrix to determine the convergent and discriminant validity of measures of attention-deficit hyperactivity disorder (ADHD)-inattention (IN), ADHD-hyperactivity/impulsivity (HI), and oppositional defiant disorder (ODD) in 917 Malaysian elementary school children. The three trait factors were ADHD-IN, ADHDHI, and ODD. The two source factors were parents and teachers. Similar to earlier studies with Australian and Brazilian children, the parent and teacher measures failed to show convergent and discriminant validity with Malaysian children. The study outlines the implications of such strong source effects in ADHD-IN, ADHD-HI, and ODD measures for the use of such parent and teacher scales to study the symptom dimensions.
Matched MeSH terms: Attention Deficit Disorder with Hyperactivity/diagnosis; Attention Deficit Disorder with Hyperactivity/ethnology*; Attention Deficit Disorder with Hyperactivity/epidemiology; Attention Deficit and Disruptive Behavior Disorders/diagnosis; Attention Deficit and Disruptive Behavior Disorders/ethnology*; Attention Deficit and Disruptive Behavior Disorders/epidemiology
Having the benefits of being environmentally friendly, providing a mild environment for bioseparation, and scalability, aqueous two-phase systems (ATPSs) have increasingly caught the attention of industry and researchers for their application in the isolation and recovery of bioproducts. The limitations of conventional ATPSs give rise to the development of temperature-induced ATPSs that have distinctive thermoseparating properties and easy recyclability. This review starts with a brief introduction to thermoseparating ATPSs, including its history, unique characteristics and advantages, and lastly, key factors that influence partitioning. The underlying mechanism of temperature-induced ATPSs is covered together with a summary of recent applications. Thermoseparating ATPSs have been proven as a solution to the demand for economically favorable and environmentally friendly industrial-scale bioextraction and purification techniques.
Napping/siesta during the day is a phenomenon, which is widely practised in the world. However, the timing, frequency, and duration may vary. The basis of napping is also diverse, but it is mainly done for improvement in alertness and general well-being. Neuroscience reveals that midday napping improves memory, enhances alertness, boosts wakefulness and performance, and recovers certain qualities of lost night sleep. Interestingly, Islam, the religion of the Muslims, advocates midday napping primarily because it was a practice preferred by Prophet Muhammad (pbuh). The objectives of this review were to investigate and compare identical key points on focused topic from both neuroscientific and Islamic perspectives and make recommendations for future researches.
The pursuit for higher degrees is accelerating in the country. With mushrooming foreign and local graduates from non-university and university status institutions, it is critical to explore the types of qualification awarded and the existing platform for recognition and accreditation purposes. The objectives of this study are: (i) to gather information with regard to current policies and practices pertaining to recognition and accreditation systems of the higher education sector, with specific reference to Malaysia and china (ii) to review the existing policy between accreditation and recognition agencies/providers and (iii) to recommend best practices, guidelines and strategies for practical implementation in Malaysia. The methodology pursuit in Malaysia and china involved inspection of documents and purposive interviews. The research was implemented from May 2009 to november 2009. The results of the research revealed that though the worldview of mutual recognition agreement is to liberalise the education sector, the authentic situations prevailing in the country requires the purposive liberalization of the education sector, with periodic reviews for its appropriateness and relevance for the needs of the country (provisional and conditional), thereby ensuring regulatory, review and quality sustainability. The customized regulatory framework would be a prerequisite (conditional), with due attention be given to either implicit or explicit conditions in the recognition of academic degrees. In deliberating the mutual recognition agreement with jurisdiction including those which are more educationally advanced, selective emerging 'niche' areas and/or supportive (conditional) have been proposed. Finally, to strengthen the existing regulatory frame work, innovative provision in this legal framework is recommended.
Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.
As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules.
Sports coaching and especially high performance coaching has long existed in some sort of duality. On one hand, sport coaching has been regarded by many as a prestigious and rewarding job, whereas on the other, sport coaching still lacks a reputation as a career opportunity mostly due to the fact that coaching is yet to receive its full professional recognition in the society. Given the vast variety of coaching qualifications, coaching roles and coaching occupations available within sport infrastructure in the society, the situation has got progressively complicated with the recognition of coaching qualifications. In addition, the growing popularity of high performance and participation sports in the society started drawing more attention from the public to the issues of coach education, competence and qualifications. Malaysian scenario on the issue is quite complicated as well, and growing demand to uplift the country’s performance in SEA, Asian, Commonwealth and Olympic Games requires interference from the higher education institutions and NGOs.
Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.