Search results for: interval features

Authors: Snehal G. Teli, R. J. Shelke

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CNN and MultiUNet models are the framework for the proposed method for enhancing and reconstructing underwater images. Multiscale merging of features and regeneration are both performed by the MultiUNet. CNN collects relevant features. Extensive tests on benchmark datasets show that the proposed strategy performs better than the latest methods. As a result of this work, underwater images can be represented and interpreted in a number of underwater applications with greater clarity. This strategy will advance underwater exploration and marine research by enhancing real-time underwater image processing systems, underwater robotic vision, and underwater surveillance.

Keywords: convolutional neural network, image enhancement, machine learning, multiunet, underwater images

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Authors: Mai Ashraf Talaat

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Bullying is an aggressive behavior and a serious issue that should be addressed by everyone and should be avoided at all costs. It is very common among adolescents and schoolchildren and the effects can be devastating and long-lasting. Students are most commonly bullied about physical appearance, race, gender, disability, ethnicity, religion, and sexual orientation. Appearance-targeted bullying is a form of bullying that targets an aspect of a person's appearance, which includes facial and dental features. Deviation from accepted dentofacial aesthetics leads to elevated incidences of bullying in schoolchildren. The aim of this review article is to assess the prevalence of bullying due to dentofacial characteristics and evaluate the importance of dentofacial appearance on perceived social attractiveness based on multiple studies.

Keywords: dentofacial features, orthodontics, malocclusion, adolescents, bullying

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Authors: Reza Gheisari, Paulo J. Bartolo, Nicholas Goddard

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Polymeric micro-cantilevers (Cs) are rapidly becoming popular for MEMS applications such as chemo- and bio-sensing as well as purely electromechanical applications such as microrelays. Polymer materials present suitable physical and chemical properties combined with low-cost mass production. Hence, micro-cantilevers made of polymers indicate much more biocompatibility and adaptability of rapid prototyping along with mechanical properties. This research studies the effects of three process and one size factors on the filling behaviour in micro cavity, and the role of each in the replication of micro parts using different polymer materials i.e. polypropylene (PP) SABIC 56M10 and acrylonitrile butadiene styrene (ABS) Magnum 8434. In particular, the following factors are considered: barrel temperature, mould temperature, injection speed and the thickness of micro features. The study revealed that the barrel temperature and the injection speed are the key factors affecting the flow length of micro features replicated in PP and ABS. For both materials, an increase of feature sizes improves the melt flow. However, the melt fill of micro features does not increase linearly with the increase of their thickness.

Keywords: flow length, micro cantilevers, micro injection moulding, microfabrication

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Authors: Dennis Infante

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This paper examined the reflections of teachers on curriculum reforms, the implementation of the K-12 Basic Education Program in the Philippines. The results revealed that problems and concerns raised by teachers could be classified into curriculum materials and design; competence, readiness and motivation of the teachers; the learning environment, and support systems; readiness, competence and motivation of students; and other relevant factors. The best features of the K-12 curriculum reforms included (1) the components, curriculum materials; (2) the design, structure and delivery of the lessons; (3) the framework and theoretical approach; (3) the qualities of the teaching-learning activities; (4) and other relevant features. With the demanding task of implementing the new curriculum, the teachers expressed their needs which included (1) making the curriculum materials available to achieve the goals of the curriculum reforms; (2) enrichment of the learning environments; (3) motivating and encouraging the teachers to embrace change; (4) providing appropriate support systems; (5) re-tooling, and empowering teachers to implement the curriculum reforms; and (6) other relevant factors. The research concluded with a synthesis that provided a paradigm for implementing curriculum reforms which recognizes the needs of the teachers and the features of the new curriculum.

Keywords: curriculum reforms, K-12, teachers' reflections, implementing curriculum change

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Authors: Tingting Chen, Shouliang Zhao

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Compared with other regions, the construction for villages and towns in regions inhabited by minority nationality shall be deeply rooted in natural and cultural endowment in locality, and more importance shall be attached to building of characteristics. In this kind of area, landscape design is very important for its character and tradition. By empirical study in Shennongjia Area, some findings could be summarized as below. There are unique natural and cultural resources in Shennongjia Forestry District; during transformation on style and features of Tujia Village, Xiaguping, special style and features have been successfully shaped through 4 strategies: (1) highlighting Tujia Culture and architectural style in west region of Hubei Province; (2) merging with local natural environment; (3) introducing system of rural coordination architect; and (4) making great efforts to design and construct environmental embellishments with village and town symbols.

Keywords: rural coordination architect, special style and features, characteristic landscape, villages and towns in regions inhabited by minority nationality

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Authors: Seham El Kareh, Miramar Etman

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Recently, detecting liars and extracting features which distinguish them from truth-tellers have been the focus of a wide range of disciplines. To the author’s best knowledge, most of the work has been done on facial expressions and body gestures but only few works have been done on the language used by both liars and truth-tellers. This paper sheds light on four axes. The first axis copes with building an audio corpus for deceptive and truthful speech for Egyptian Arabic speakers. The second axis focuses on examining the human perception of lies and proving our need for computational linguistic-based methods to extract features which characterize truthful and deceptive speech. The third axis is concerned with building a linguistic analysis program that could extract from the corpus the inter- and intra-linguistic cues for deceptive and truthful speech. The program built here is based on selected categories from the Linguistic Inquiry and Word Count program. Our results demonstrated that Egyptian Arabic speakers on one hand preferred to use first-person pronouns and present tense compared to the past tense when lying and their lies lacked of second-person pronouns, and on the other hand, when telling the truth, they preferred to use the verbs related to motion and the nouns related to time. The results also showed that there is a need for bigger data to prove the significance of words related to emotions and numbers.

Keywords: Egyptian Arabic corpus, computational analysis, deceptive features, forensic linguistics, human perception, truthful features

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Authors: Komathy Thiagarajan, Mohd. Azizuddin Mohd. Yussof, Hasnoorina Husin, Noor Azreena Abd Aziz, Faezah Shekh Abdullah, Abdul Wahaf Abdul Wahid

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Polycystic Ovary Syndrome (PCOS) presents with a plethora of clinical features owing to the multifaceted underlying pathophysiology. This study was conducted to determine the clinical features unique to the sub fertile women attending the Sub fertility Clinic of the National Population and Family Development Board (NPFDB) so that a more holistic approach can be adopted to further enhance the pregnancy outcome in those women. This was a case-control study conducted over a span of three years (from January 2014 until December 2016), whereby women who fulfilled the Rotterdam Criteria 2004 were classified as PCOS (n=79) and women who did not fulfill the Rotterdam Criteria were classified as controls (n=88). The mean age of the women was 30.1 years and the mean duration of marriage was 3.93 years. The majority of women suffered from primary sub fertility (82.6%). The median age was lower among PCOS women (29.0 years) compared to the controls (30.0 years), p<0.05. The majority of PCOS women (43.0%) were obese (BMI > 30 kg/m2) compared to only 19.3% who were obese in the control group, p<0.05. Hypertension was present in 59.5% of PCOS women and only in 36.4% of the control group, p<0.05. There were significantly more women who presented with hirsutism in PCOS group (27.8%) as compared to the control group (5.7%), p<0.05. The findings of this study elucidate that the clinical features of significance among sub fertile women suffering from PCOS, if detected early, are amenable to lifestyle modifications and timely interventions can potentially improve the fertility outcomes in this group of women.

Keywords: clinical features, fertility, lifestyle modification, PCOS

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Che Hsiu Chen, Su Yun Chen, Hon Wen Cheng

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Greater core stability may benefit sports performance by providing a foundation for greater force production in the upper and lower extremities. Core stability exercises on instability device (such as the TRX suspension systems) were found to be able to induce higher core muscle activity than performing on a stable surface. However, high intensity interval TRX suspension exercises training on sport performances remain unclear. The purpose of this study was to examine whether high intensity TRX suspension training could improve sport performance. Twenty-four healthy university female students (age 19.0 years, height 157.9 cm, body mass 51.3 kg, fat mass 25.2 %) were voluntarily participated in this study. After a familiarization session, each participant underwent five suspension exercises (e.g., hip abduction in plank alternative, hamstring curl, 45-degree row, lunge and oblique crunch). Each type of exercise was performed for 30 seconds, followed by 30 seconds break, two times per week for eight weeks while each exercise session was increased by 10 seconds every week. The results showed that the fat mass (about 12.92%) decreased significantly, sit and reach test (9%), 1 minute sit-up test (17.5%), standing broad jump (4.8%), physical fitness index (10.3%) increased significantly after 8-week high intensity TRX suspension training. Hence, eight weeks of high intensity interval TRX suspension exercises training can improve hamstring flexibility, trunk endurance, jump ability, aerobic fitness and fat mass percentage decreased substantially.

Keywords: core endurance, jump, flexibility, cardiovascular fitness

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Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu

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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.

Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness

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Authors: Radostina Popova

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The paper presents an analysis of the innovation performance of small and medium-sized furniture enterprises in Bulgaria, accounting for over 97% of the companies in the sector. It contains advanced features of innovation in enterprises, specific features of the furniture industry in Bulgaria and analysis of the results of studies on the topic. The results from studies of three successive periods - 2006-2008; 2008-2010; 2010-2012, during which were studied 594 small and medium-sized furniture enterprises. There are commonly used in the EU definitions and indicators (European Commission, OECD, Oslo Manual), which allows for the comparability of results.

Keywords: innovation activity, competitiveness of innovation, furniture enterprises in Bulgaria

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Authors: Saba Ebrahimi, Saeed Ahmadian, Hedie Ashrafi

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Selecting appropriate patients for surgery is one of the main issues in thoracic surgery (TS). Both short-term and long-term risks and benefits of surgery must be considered in the patient selection criteria. There are some limitations in the existing datasets of TS patients because of missing values of attributes and imbalanced distribution of survival classes. In this study, a novel ensemble architecture of deep learning networks is proposed based on stacking different linear and non-linear layers to deal with imbalance datasets. The categorical and numerical features are split using different layers with ability to shrink the unnecessary features. Then, after extracting the insight from the raw features, a novel biased-kernel layer is applied to reinforce the gradient of the minority class and cause the network to be trained better comparing the current methods. Finally, the performance and advantages of our proposed model over the existing models are examined for predicting patient survival after thoracic surgery using a real-life clinical data for lung cancer patients.

Keywords: deep learning, ensemble models, imbalanced classification, lung cancer, TS patient selection

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Authors: Baha Hasan, Jan Wikander

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This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.

Keywords: assembly feature, assembly process planning, feature, feature-based modelling, form feature, ontology

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Authors: Rady A., Elsheshai A., Elsawy M., Nagui R.

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Background and Aim: Metabolic syndrome affect around 20% of general population , authors have incriminated antipsychotics as serious risk factor that may provoke such derangement. The aim of our study is to assess metabolic syndrome in patients presenting psychotic features (delusions and hallucinations) whether schizophrenia or mood disorder and compare results in terms of drug naïf, on medication and healthy control. Subjects and Methods: The study recruited 40 schizophrenic patients, half of them drug naïf and the other half on antipsychotics, 40 patients with mood disorder with psychotic features, half of them drug naïf and the other half on medication, 20 healthy control. Exclusion criteria were put in order to exclude patients having already endocrine or metabolic disorders that my interfere with results obtain to minimize confusion bias. Metabolic syndrome assessed by measuring parameters including weight, body mass index, waist circumference, triglyceride level, HDL, fasting glucose, fasting insulin and insulin resistance Results: No difference was found when comparing drug naïf to those on medication in both schizophrenic and psychotic mood disorder arms, schizophrenic patients whether on medication or drug naïf should difference with control group for fasting glucose, schizophrenic patients on medication also showed difference in insulin resistance compared to control group. On the other hand, patients with psychotic mood disorder whether drug naïf or on medication showed difference from control group for fasting insulin level. Those on medication also differed from control for insulin resistance Conclusion: Our study didn’t reveal difference in metabolic syndrome among patients with psychotic features whether on medication or drug naïf. Only patients with Psychotic features on medication showed insulin resistance. Schizophrenic patients drug naïf or on medication tend to show higher fasting glucose while psychotic mood disorder whether drug naïf or on medication tend to show higher fasting insulin. This study suggest that presence of psychotic features themselves regardless being on medication or not carries a risk for insulin resistance and metabolic syndrome. Limitation: This study is limited by number of participants and larger numbers in future studies should be included in order to extrapolate results. Cohort longitudinal studies are needed in order to evaluate such hypothesis.

Keywords: schizophrenia, metabolic syndrome, psychosis, insulin, resistance

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Authors: Shao Bo Cheng, Yong-Jin Han, Se Young Park, Seong-Bae Park

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Consumer-to-Consumer (C2C) E-commerce has been growing at a very high speed in recent years. Since identical or nearly-same kinds of products compete one another by relying on keyword search in C2C E-commerce, some sellers describe their products with spam keywords that are popular but are not related to their products. Though such products get more chances to be retrieved and selected by consumers than those without spam keywords, the spam keywords mislead the consumers and waste their time. This problem has been reported in many commercial services like e-bay and taobao, but there have been little research to solve this problem. As a solution to this problem, this paper proposes a method to classify whether keywords of a product are spam or not. The proposed method assumes that a keyword for a given product is more reliable if the keyword is observed commonly in specifications of products which are the same or the same kind as the given product. This is because that a hierarchical category of a product in general determined precisely by a seller of the product and so is the specification of the product. Since higher layers of the hierarchical category represent more general kinds of products, a reliable degree is differently determined according to the layers. Hence, reliable degrees from different layers of a hierarchical category become features for keywords and they are used together with features only from specifications for classification of the keywords. Support Vector Machines are adopted as a basic classifier using the features, since it is powerful, and widely used in many classification tasks. In the experiments, the proposed method is evaluated with a golden standard dataset from Yi-han-wang, a Chinese C2C e-commerce, and is compared with a baseline method that does not consider the hierarchical category. The experimental results show that the proposed method outperforms the baseline in F1-measure, which proves that spam keywords are effectively identified by a hierarchical category in C2C e-commerce.

Keywords: spam keyword, e-commerce, keyword features, spam filtering

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Authors: B. Samuel Raj, Solomon R. D. Jebakumar

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Microbial fuel cells (MFCs) are an emerging and promising method for achieving sustainable energy since they can remove contaminated organic matter and simultaneously generate electricity. Our approach was driven in three different ways like Bacterial fuel cell, Soil Microbial fuel cell (Soil MFC) and Plant Microbial fuel cell (Plant MFC). Bacterial MFC: Sulphate reducing bacteria (SRB) were isolated and identified as the efficient electricigens which is able to produce ±2.5V (689mW/m2) and it has sustainable activity for 120 days. Experimental data with different MFC revealed that high electricity production harvested continuously for 90 days 1.45V (381mW/m2), 1.98V (456mW/m2) respectively. Biofilm formation was confirmed on the surface of the anode by high content screening (HCS) and scanning electron Microscopic analysis (SEM). Soil MFC: Soil MFC was constructed with low cost and standard Mudwatt soil MFC was purchased from keegotech (USA). Vermicompost soil (V1) produce high energy (± 3.5V for ± 400 days) compared to Agricultural soil (A1) (± 2V for ± 150 days). Biofilm formation was confirmed by HCS and SEM analysis. This finding provides a method for extracting energy from organic matter, but also suggests a strategy for promoting the bioremediation of organic contaminants in subsurface environments. Our Soil MFC were able to run successfully a 3.5V fan and three LED continuously for 150 days. Plant MFC: Amaranthus candatus (P1) and Triticum aestivium (P2) were used in Plant MFC to confirm the electricity production from plant associated microbes, four uniform size of Plant MFC were constructed and checked for energy production. P2 produce high energy (± 3.2V for 40 days) with harvesting interval of two times and P1 produces moderate energy without harvesting interval (±1.5V for 24 days). P2 is able run 3.5V fan continuously for 10days whereas P1 needs optimization of growth conditions to produce high energy.

Keywords: microbial fuel cell, biofilm, soil microbial fuel cell, plant microbial fuel cell

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Authors: Ayat Adnan Atwah, Muhammad A. Khan

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Self-cleaning surfaces are getting significant attention in industrial fields. Especially for textile fabrics, it is observed that self-cleaning textile fabric surfaces are created by manipulating the surface features with the help of coatings and nanoparticles, which are considered costly and far more complicated. However, controlling the fabrication parameters of textile fabrics at the microscopic level by exploring the potential for self-cleaning has not been addressed. This study aimed to establish the context of self-cleaning textile fabrics by controlling the fabrication parameters of the textile fabric at the microscopic level. Therefore, 3D-printed textile fabrics were fabricated using the low-cost fused filament fabrication (FFF) technique. The printing parameters, such as orientation angle (O), layer height (LH), and extruder width (EW), were used to control the microscopic features of the printed fabrics. The combination of three printing parameters was created to provide the best self-cleaning textile fabric surface: (LH) (0.15, 0.13, 0.10 mm) and (EW) (0.5, 0.4, 0.3 mm) along with two different (O) of (45º and 90º). Three different thermoplastic flexible filament materials were used: (TPU 98A), (TPE felaflex), and (TPC flex45). The printing parameters were optimised to get the optimum self-cleaning ability of the printed specimens. Furthermore, the impact of these characteristics on mechanical strength at the fabric-woven structure level was investigated. The study revealed that the printing parameters significantly affect the self-cleaning properties after adjusting the selected combination of layer height, extruder width, and printing orientation. A linear regression model was effectively developed to demonstrate the association between 3D printing parameters (layer height, extruder width, and orientation). According to the experimental results, (TPE felaflex) has a better self-cleaning ability than the other two materials.

Keywords: 3D printing, self-cleaning fabric, microscopic features, printing parameters, fabrication

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Authors: Raid R. O. Al-Nima, S. S. Dlay, W. L. Woo

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A relationship between face and signature biometrics is established in this paper. A new approach is developed to predict faces from signatures by using artificial intelligence. A multilayer perceptron (MLP) neural network is used to generate face details from features extracted from signatures, here face is the physical biometric and signatures is the behavioural biometric. The new method establishes a relationship between the two biometrics and regenerates a visible face image from the signature features. Furthermore, the performance efficiencies of our new technique are demonstrated in terms of minimum error rates compared to published work.

Keywords: behavioural biometric, face biometric, neural network, physical biometric, signature biometric

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Authors: Shao-Tzu Huang, Chen-Chien Hsu, Wei-Yen Wang

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Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs.

Keywords: feature matching, k-means clustering, SIFT, RANSAC

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Authors: A: Annis Fathima, V. Vaidehi, S. Ajitha

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Face recognition system finds many applications in surveillance and human computer interaction systems. As the applications using face recognition systems are of much importance and demand more accuracy, more robustness in the face recognition system is expected with less computation time. In this paper, a hybrid approach for face recognition combining Gabor Wavelet and Linear Discriminant Analysis (HGWLDA) is proposed. The normalized input grayscale image is approximated and reduced in dimension to lower the processing overhead for Gabor filters. This image is convolved with bank of Gabor filters with varying scales and orientations. LDA, a subspace analysis techniques are used to reduce the intra-class space and maximize the inter-class space. The techniques used are 2-dimensional Linear Discriminant Analysis (2D-LDA), 2-dimensional bidirectional LDA ((2D)2LDA), Weighted 2-dimensional bidirectional Linear Discriminant Analysis (Wt (2D)2 LDA). LDA reduces the feature dimension by extracting the features with greater variance. k-Nearest Neighbour (k-NN) classifier is used to classify and recognize the test image by comparing its feature with each of the training set features. The HGWLDA approach is robust against illumination conditions as the Gabor features are illumination invariant. This approach also aims at a better recognition rate using less number of features for varying expressions. The performance of the proposed HGWLDA approaches is evaluated using AT&T database, MIT-India face database and faces94 database. It is found that the proposed HGWLDA approach provides better results than the existing Gabor approach.

Keywords: face recognition, Gabor wavelet, LDA, k-NN classifier

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Authors: Seyed Nima Tayarani Bathaie

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In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined.

Keywords: face detection algorithm, Haar features, security of ATM

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Authors: Bjorn-Ole Meyer, Dominik Marti, Peter E. Andersen

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A multimodal imaging system, combining spectrally resolved multiphoton microscopy (MPM) and optical coherence microscopy (OCM) is demonstrated. MPM and OCM are commonly integrated into multimodal imaging platforms to combine functional and morphological information. The MPM signals, such as two-photon fluorescence emission (TPFE) and signals created by second harmonic generation (SHG) are biomarkers which exhibit information on functional biological features such as the ratio of pyridine nucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD) in the classification of cancerous tissue. While the spectrally resolved imaging allows for the study of biomarkers, using a spectrometer as a detector limits the imaging speed of the system significantly. To overcome those limitations, an OCM setup was added to the system, which allows for fast acquisition of structural information. Thus, after rapid imaging of larger specimens, navigation within the sample is possible. Subsequently, distinct features can be selected for further investigation using MPM. Additionally, by probing a different contrast, complementary information is obtained, and different biomarkers can be investigated. OCM images of tissue and cell samples are obtained, and distinctive features are evaluated using MPM to illustrate the benefits of the system.

Keywords: optical coherence microscopy, multiphoton microscopy, multimodal imaging, two-photon fluorescence emission

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: Wei-Jong Yang, Yu-Siang Su, Pau-Choo Chung, Jar-Ferr Yang

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Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones.

Keywords: moving object detection, histogram of oriented gradient, histogram of uniformly-oriented gradient, linear support vector machine

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Authors: Fergus Wade, Jasmine Makker, Matthew Jankinson, Aminah Qamar, Gemma Morrelli, Shayan Shah

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Background: Ambulance offload delays (AODs) affect patient outcomes. At baseline, the average AOD at Princess Royal University Hospital (PRUH) was 41 minutes, in breach of the 15-minute target. Aims: By February 2023, we aimed to reduce: the average AOD to 30 minutes percentage of AOD >30 minutes (PA30) to 25% and >60 minutes (PA60) to 10% Methods: Following a root-cause analysis, we implemented 2 Plan, Do, Study, Act (PDSA) cycles. PDSA-1 ‘Drop-and-run’: ambulances waiting >15 minutes for a handover left the patients in the Emergency Department (ED) and returned to the community. PDSA-2: Booking in the patients before the handover, allowing direct updates to online records, eliminating the need for handwritten notes. Outcome measures: AOD, PA30, and PA60, and process measures: total ambulances and patients in the ED were recorded for 16 weeks. Results: In PDSA-1, all parameters increased slightly despite unvarying ED crowding. In PDSA-2, two shifts in data were seen: initially, a sharp increase in the outcome measures consistent with increased ED crowding, followed by a downward shift when crowding returned to baseline (p<0.01). Within this interval, the AOD reduced to 29.9 minutes, and PA30 and PA60 were 31.2% and 9.2% respectively. Discussion/conclusion: PDSA-1 didn’t result in any significant changes; lack of compliance was a key cause. The initial upward shift in PDSA-2 is likely associated with NHS staff strikes. However, during the second interval, the AOD and the PA60 met our targets of 30 minutes and 10%, respectively, improving patient flow in the ED. This was sustained without further input and if maintained, saves 2 paramedic shifts every 3 days.

Keywords: ambulance offload, district general hospital, handover, quality improvement

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Authors: Alan S. Hoback

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Most people see human faces in car front and back ends because of the process of pareidolia. 96 people were surveyed to see how many of them saw a face in the vehicle styling. Participants were aged 18 to 72 years. 94% of the participants saw faces in the front-end design of production models. All participants that recognized faces indicated that most styles showed some degree of an angry expression. It was found that women were more likely to see faces in inanimate objects. However, with respect to whether women were more likely to perceive anger in the vehicle design, the results need further clarification. Survey responses were correlated to the design features of vehicles to determine what cues the respondents were likely looking at when responding. Whether the features looked anthropomorphic was key to anger perception. Features such as the headlights which could represent eyes and the air intake that could represent a mouth had high correlations to trends in scores. Results are compared among models, makers, by groupings of body styles classifications for the top 12 brands sold in the US, and by year for the top 20 models sold in the US in 2016. All of the top models sold increased in perception of an angry expression over the last 20 years or since the model was introduced, but the relative change varied by body style grouping.

Keywords: aggressive driving, face recognition, road rage, vehicle styling

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Authors: Ammar Khader Almasri

Abstract:

The software processes play a vital role for delivering a high quality software system that meets the user’s needs. There are many software development models which are used by most system developers, which can be categorized into two categories (traditional and new methodologies). Mobile applications like other desktop applications need appropriate and well-working software development process. Nevertheless, mobile applications have different features which limit their performance and efficiency like application size, mobile hardware features. Moreover, this research aims to help developers in using a standardized model for developing mobile applications.

Keywords: software development process, agile methods , moblile application development, traditional methods

Procedia PDF Downloads 387

Authors: Manuela Nayantara Jeyaraj

Abstract:

PostgreSQL is an Object-Relational Database Management System (ORDBMS) with an architecture that ensures optimal quality data management. But due to the shading growth of similar ORDBMS, PostgreSQL has not been renowned among the database user community. Despite having its features and in-built functionalities shadowed, PostgreSQL renders a vast range of utilities for data manipulation and hence calling for it to be upheld more among users. But introducing PostgreSQL in order to stimulate its advantageous features among users, mandates endorsing learn-ability as an add-on as the target groups considered consist of both amateur as well as professional PostgreSQL users. The scope of this paper deliberates providing easy contemplation of query formulations and flows through a visual editor designed according to user interface principles that standby to support every aspect of making PostgreSQL learn-able by self-operation and creation of queries within the visual editor. This paper tends to scrutinize the importance of choosing PostgreSQL as the working database environment, the visual perspectives that influence human behaviour and ultimately learning, the modes in which learn-ability can be provided via visualization and the advantages reaped by the implementation of the proposed system features.

Keywords: database, learn-ability, PostgreSQL, query, visual-editor

Procedia PDF Downloads 174

Authors: Jinseon Song, Yongwan Park

Abstract:

In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction.

Keywords: positioning, distance, camera, features, SURF(Speed-Up Robust Features), database, estimation

Procedia PDF Downloads 349