Search results for: statistical features

Authors: Yin Yuanling

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A Deep Feedback Recurrent Neural Network (DFRNN) and Bidirectional Long Short-Term Memory (BiLSTM) are designed to address the problem of low accuracy of traditional relationship extraction models. This method combines a deep feedback-based recurrent neural network (DFRNN) with a bi-directional long short-term memory (BiLSTM) approach. The method combines DFRNN, which extracts local features of text based on deep feedback recurrent mechanism, BiLSTM, which better extracts global features of text, and Self-Attention, which extracts semantic information. Experiments show that the method achieves an F1 value of 76.69% on the CEC dataset, which is 0.0652 better than the BiLSTM+Self-ATT model, thus optimizing the performance of the deep learning method in the event relationship extraction task.

Keywords: event relations, deep learning, DFRNN models, bi-directional long and short-term memory networks

Procedia PDF Downloads 115

Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: Raghad Dwaik

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It is well known in language acquisition literature that a mere understanding of a reading text is not enough to help students build proficiency in comprehension. Students should rather follow understanding by attempting to express what has been understood by pushing their competence to the limit. Learners' attempt to push their competence was given the term "comprehensible output" by Swain (1985). Teachers in large classes, however, find it sometimes difficult to give all students a chance to communicate their views or to share their ideas during the short class time. In most cases, students who are outgoing dominate class discussion and get more opportunities for practice which leads to ignoring the shy students totally while helping the good ones become better. This paper presents the idea of using Google Classroom features of posting and commenting to allow students who hesitate to participate in class discussions about a reading text to write their views on the wall of a Google Classroom and share them later after they have received feedback and comments from classmates. Such attempts lead to developing their proficiency through additional practice in comprehensible output and to enhancing their confidence in themselves and their views. It was found that virtual classroom interaction would help students maintain vocabulary, use more complex structures and focus on meaning besides form.

Keywords: learning groups, reading TESOL, Google Classroom, comprehensible output

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Authors: Woei-Shyan Lee, Hao-Chien Kao

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The impact deformation and fracture behaviour of cobalt-based Haynes 188 superalloy are investigated by means of a split Hopkinson pressure bar. Impact tests are performed at strain rates ranging from 1×103 s-1 to 5×103 s-1 and temperatures between 25°C and 800°C. The experimental results indicate that the flow response and fracture characteristics of cobalt-based Haynes 188 superalloy are significantly dependent on the strain rate and temperature. The flow stress, work hardening rate and strain rate sensitivity all increase with increasing strain rate or decreasing temperature. It is shown that the impact response of the Haynes 188 specimens is adequately described by the Zerilli-Armstrong fcc model. The fracture analysis results indicate that the Haynes 188 specimens fail predominantly as the result of intensive localised shearing. Furthermore, it is shown that the flow localisation effect leads to the formation of adiabatic shear bands. The fracture surfaces of the deformed Haynes 188 specimens are characterised by dimple- and / or cleavage-like structure with knobby features. The knobby features are thought to be the result of a rise in the local temperature to a value greater than the melting point.

Keywords: Haynes 188 alloy, impact, strain rate and temperature effect, adiabatic shearing

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Authors: Sarah E. Nelson, Casey Weiner, Alexander Sigmon, Jun Hua, Haris I. Sair, Jose I. Suarez, Robert D. Stevens

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Graph-theoretical information from structural connectomes indicated significant connectivity changes and improved acute prognostication in a Random Forest (RF) model in aneurysmal subarachnoid hemorrhage (aSAH), which can lead to significant morbidity and mortality and has traditionally been fraught by poor methods to predict outcome. This study’s hypothesis was that structural connectivity changes occur in canonical brain networks of acute aSAH patients, and that these changes are associated with functional outcome at six months. In a prospective cohort of patients admitted to a single institution for management of acute aSAH, patients underwent diffusion tensor imaging (DTI) as part of a multimodal MRI scan. A weighted undirected structural connectome was created of each patient’s images using Constant Solid Angle (CSA) tractography, with 176 regions of interest (ROIs) defined by the Johns Hopkins Eve atlas. ROIs were sorted into four networks: Default Mode Network, Executive Control Network, Salience Network, and Whole Brain. The resulting nodes and edges were characterized using graph-theoretic features, including Node Strength (NS), Betweenness Centrality (BC), Network Degree (ND), and Connectedness (C). Clinical (including demographics and World Federation of Neurologic Surgeons scale) and graph features were used separately and in combination to train RF and Logistic Regression classifiers to predict two outcomes: dichotomized modified Rankin Score (mRS) at discharge and at six months after discharge (favorable outcome mRS 0-2, unfavorable outcome mRS 3-6). A total of 56 aSAH patients underwent DTI a median (IQR) of 7 (IQR=8.5) days after admission. The best performing model (RF) combining clinical and DTI graph features had a mean Area Under the Receiver Operator Characteristic Curve (AUROC) of 0.88 ± 0.00 and Area Under the Precision Recall Curve (AUPRC) of 0.95 ± 0.00 over 500 trials. The combined model performed better than the clinical model alone (AUROC 0.81 ± 0.01, AUPRC 0.91 ± 0.00). The highest-ranked graph features for prediction were NS, BC, and ND. These results indicate reorganization of the connectome early after aSAH. The performance of clinical prognostic models was increased significantly by the inclusion of DTI-derived graph connectivity metrics. This methodology could significantly improve prognostication of aSAH.

Keywords: connectomics, diffusion tensor imaging, graph theory, machine learning, subarachnoid hemorrhage

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Authors: Imen Elleuch, Wael Ouarda, Gargouri Bilel

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We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process.

Keywords: deep-learning approach, object-classes, semantic classification, Arabic

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Authors: Anna D. Kotrova, Alexandr N. Shishkin, Elena I. Ermolenko

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The aim. To study the quantitative and qualitative colon bacteria ratio from patients with metabolic syndrome. Materials and methods. Fecal samples from patients of 2 groups were identified and analyzed: the first group was formed by patients with metabolic syndrome, the second one - by healthy individuals. The metagenomics method was used with the analysis of 16S rRNA gene sequences. The libraries of the variable sites (V3 and V4) gene 16S RNA were analyzed using the MiSeq device (Illumina). To prepare the libraries was used the standard recommended by Illumina, a method based on two rounds of PCR. Results. At the phylum level in the microbiota of patients with metabolic syndrome compared to healthy individuals, the proportion of Tenericutes was reduced, the proportion of Actinobacteria was increased. At the genus level, in the group with metabolic syndrome, relative to the second group was increased the proportion of Lachnospira. Conclusion. Changes in the colon bacteria ratio in the gut microbiota of patients with metabolic syndrome were found both at the type and the genus level. In the metabolic syndrome group, there is a decrease in the proportion of bacteria that do not have a cell wall. To confirm the revealed microbiota features in patients with metabolic syndrome, further study with a larger number of samples is required.

Keywords: gut microbiota, metabolic syndrome, metagenomics, tenericutes

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Authors: Qasim A. El Dwairi, Saleh M. Banihani, Ayat S. Banihani, Ziad M. Bataineh

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Neurons in the spinal trigeminal nucleus of the camel were studied by Golgi impregnation. Neurons were classified based on differences in size and shape of their cell bodies, density of their dendritic trees, morphology and distribution of their appendages. In the spinal trigeminal nucleus of the camel, at least twelve types of neurons were identified. These neurons include, stalked, islets, octubus-like, lobulated, boat-like, pyramidal, multipolar, round, oval and elongated neurons. They have large number of different forms of appendages not only for their dendrites but also for their cell bodies. Neurons with unique large dilatations especially at their dendritic branching points were found. The morphological features of these neurons were described and compared with their counterparts in other species. Finding of large number of neuronal types with different size and shapes and large number of different forms of appendages for cell bodies and dendrites together with the presence of cells with unique features such as large dilated parts for dendrites may indicate to a very complex information processing for pain and temperature at the level of the spinal trigeminal nucleus in the camel that traditionally live in a very hard environment (the desert).

Keywords: camel, golgi, neurons , spinal trigeminal nucleus

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Authors: Isobel Hambleton, Mark Carrington

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Trypanosoma brucei, the causal agent of a range of diseases in humans and livestock, evades the mammalian immune system through a population survival strategy based on the expression of a series of antigenically distinct variant surface glycoproteins (VSGs). RNAi mediated knockdown of the active VSG gene triggers a precytokinesis cell cycle arrest. To determine whether this phenotype is the result of reduced VSG transcript or depleted VSG protein, we used morpholino antisense oligonucleotides to block translation of VSG mRNA. The same precytokinesis cell cycle arrest was observed, suggesting that VSG protein abundance is monitored closely throughout the cell cycle. An inducible expression system has been developed to test various GPI-anchored proteins for their ability to rescue this cell cycle arrest. This system has been used to demonstrate that wild-type VSG expressed from a T7 promoter rescues this phenotype. This indicates that VSG expression from one of the specialised bloodstream expression sites (BES) is not essential for cell division. The same approach has been used to define the minimum essential features of a VSG necessary for function.

Keywords: bloodstream expression site, morpholino, precytokinesis cell cycle arrest, variant surface glycoprotein

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Authors: Van Thinh Bui, Anh Tuan Tran, Quoc Viet Ngo, The Bao Pham

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Video retrieval is a searching problem on videos or clips based on content in which they are relatively close to an input image or video. The application of this retrieval consists of selecting video in a folder or recognizing a human in security camera. However, some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. In order to overcome all obstacles, we propose a content-based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is performed on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show. The performance is evaluated in promising comparison to the other approaches.

Keywords: video retrieval, principal objects, keyframe, segmentation of aggregating superpixels, speeded up robust features, bag-of-words, SVM

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Authors: Bo Wang

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As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM.

Keywords: ZY-3 satellite imagery, DEM, SRTM, refinement

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Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

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Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

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Authors: Farhad Shafiepour Motlagh, Narges Salehi

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The aim of the present study was to investigate the relationship between inspirational leadership style and perceived social capital through the mediation of organizational knowledge resource development. The research method was descriptive-correlational. The statistical population consisted of all 3537 secondary school teachers in Isfahan. Sample selection was based on Cochran's formula volume formula for 338 people and multi-stage random sampling. The research instruments included a researcher-made inspirational leadership style questionnaire, a perceived social capital questionnaire (Putnam, 1999), and a researcher-made questionnaire of perceived organizational knowledge resources. Kolmogorov statistical tests, Pearson correlation, stepwise multiple regression, and structural equation modeling were used to analyze the data. In general, the results showed that there is a significant relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05. Also, the development of organizational knowledge resources mediates the relationship between inspirational leadership style and the use of perceived social capital at the level of P <0.05.

Keywords: inspirational leadership style, perceived social capital, perceived organizational knowledge

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Authors: Azam Tabatabaee, Fereshteh Dastgoshadeh, Akram Tabatabaee

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This paper describes the use of by-products as adsorbents for removing heavy metals from aqueous effluent solutions. Products of almond skin, walnut shell, saw dust, rice bran and egg shell were evaluated as metal ion adsorbents in aqueous solutions. A comparative study was done with commercial adsorbents like ion exchange resins and activated carbon too. Batch experiments were investigated to determine the affinity of all of biomasses for, Cd(ΙΙ), Cr(ΙΙΙ), Ni(ΙΙ), and Pb(ΙΙ) metal ions at pH 5. The rate of metal ion removal in the synthetic wastewater by the biomass was evaluated by measuring final concentration of synthetic wastewater. At a concentration of metal ion (50 mg/L), egg shell adsorbed high levels (98.6 – 99.7%) of Pb(ΙΙ) and Cr(ΙΙΙ) and walnut shell adsorbed high levels (35.3 – 65.4%) of Ni(ΙΙ) and Cd(ΙΙ). In this study, it has been shown that by-products were excellent adsorbents for removal of toxic ions from wastewater with efficiency comparable to commercially available adsorbents, but at a reduced cost. Also statistical studies using Independent Sample t Test and ANOVA Oneway for statistical comparison between various elements adsorption showed that there isn’t a significant difference in some elements adsorption percentage by by-products and commercial adsorbents.

Keywords: adsorbents, heavy metals, commercial adsorbents, wastewater, by-products

Procedia PDF Downloads 390

Authors: Areej Isam Barham

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Developing students’ number sense is an essential element in the learning of mathematics. Number sense is one of the foundational ideas in mathematics where students need to understand numbers, representing them in different ways, and realize the relationships among numbers. Number sense also reflects students’ understanding of the meaning of operations, how they related to one another, how to compute fluently and make reasonable estimates. Developing students’ number sense in the mathematics classroom requires good preparation for mathematics teachers, those who will direct their students towards the real understanding of numbers and its implementation in the learning of mathematics. This study describes the development of elementary prospective mathematics teachers’ number sense through a mathematics teaching methods course at Qatar University. The study examined the effect of using the Lesh model in enhancing mathematics prospective teachers’ number sense. Thirty-nine elementary prospective mathematics teachers involved in the current study. The study followed an experimental research approach, and quantitative research methods were used to answer the research questions. Pre-post number sense test was constructed and implemented before and after teaching by using the Lesh model. Data were analyzed using Statistical Packages for Social Sciences (SPSS). Descriptive data analysis and t-test were used to examine the impact of using the Lesh model in enhancing prospective teachers’ number sense. Finding of the study indicated poor number sense and limited numeracy skills before implementing the use of the Lesh model, which highly demonstrate the importance of the study. The results of the study also revealed a positive impact on the use of the Lesh model in enhancing prospective teachers’ number sense with statistically significant differences. The discussion of the study addresses different features and issues related to the participants’ number sense. In light of the study, the research presents recommendations and suggestions for the future development of mathematics prospective teachers’ number sense.

Keywords: number sense, Lesh model, prospective mathematics teachers, development of number sense

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Authors: O. J. Akinsola, Yinan Zheng, Rose Anorlu, F. T. Ogunsola, Lifang Hou, Robert Leo-Murphy

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Cervical Cancer (CC) is the 2nd most common cancer among women living in low and middle-income countries, with no associated symptoms during formative periods. With the advancement and innovative medical research, there are numerous preventive measures being utilized, but the incidence of cervical cancer cannot be truncated with the application of only screening tests. The mortality associated with this invasive cervical cancer can be nipped in the bud through the important role of early-stage detection. This study research selected an array of different top features selection techniques which was aimed at developing a model that could validly diagnose the risk factors of cervical cancer. A retrospective clinic-based cohort study was conducted on 178 HIV-associated cervical cancer patients in Lagos University teaching Hospital, Nigeria (U54 data repository) in April 2022. The outcome measure was the automated prediction of the HIV-associated cervical cancer cases, while the predictor variables include: demographic information, reproductive history, birth control, sexual history, cervical cancer screening history for invasive cervical cancer. The proposed technique was assessed with R and Python programming software to produce the model by utilizing the classification algorithms for the detection and diagnosis of cervical cancer disease. Four machine learning classification algorithms used are: the machine learning model was split into training and testing dataset into ratio 80:20. The numerical features were also standardized while hyperparameter tuning was carried out on the machine learning to train and test the data. Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN). Some fitting features were selected for the detection and diagnosis of cervical cancer diseases from selected characteristics in the dataset using the contribution of various selection methods for the classification cervical cancer into healthy or diseased status. The mean age of patients was 49.7±12.1 years, mean age at pregnancy was 23.3±5.5 years, mean age at first sexual experience was 19.4±3.2 years, while the mean BMI was 27.1±5.6 kg/m2. A larger percentage of the patients are Married (62.9%), while most of them have at least two sexual partners (72.5%). Age of patients (OR=1.065, p<0.001**), marital status (OR=0.375, p=0.011**), number of pregnancy live-births (OR=1.317, p=0.007**), and use of birth control pills (OR=0.291, p=0.015**) were found to be significantly associated with HIV-associated cervical cancer. On top ten 10 features (variables) considered in the analysis, RF claims the overall model performance, which include: accuracy of (72.0%), the precision of (84.6%), a recall of (84.6%) and F1-score of (74.0%) while LR has: an accuracy of (74.0%), precision of (70.0%), recall of (70.0%) and F1-score of (70.0%). The RF model identified 10 features predictive of developing cervical cancer. The age of patients was considered as the most important risk factor, followed by the number of pregnancy livebirths, marital status, and use of birth control pills, The study shows that data mining techniques could be used to identify women living with HIV at high risk of developing cervical cancer in Nigeria and other sub-Saharan African countries.

Keywords: associated cervical cancer, data mining, random forest, logistic regression

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Authors: Sahar Heidary, Ramin Ghasemi Shayan

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By the development of new coronavirus (2019-nCoV) pneumonia, chest high-resolution computed tomography (HRCT) has been one of the main investigative implements. To realize timely and truthful diagnostics, defining the radiological features of the infection is of excessive value. The purpose of this impression was to consider the imaging demonstrations of early-stage coronavirus disease 2019 (COVID-19) and to run an imaging base for a primary finding of supposed cases and stratified interference. The right prophetic rate of HRCT was 85%, sensitivity was 73% for all patients. Total accuracy was 68%. There was no important change in these values for symptomatic and asymptomatic persons. These consequences were besides free of the period of X-ray from the beginning of signs or interaction. Therefore, we suggest that HRCT is a brilliant attachment for early identification of COVID-19 pneumonia in both symptomatic and asymptomatic individuals in adding to the role of predictive gauge for COVID-19 pneumonia. Patients experienced non-contrast HRCT chest checkups and images were restored in a thin 1.25 mm lung window. Images were estimated for the existence of lung scratches & a CT severity notch was allocated separately for each patient based on the number of lung lobes convoluted.

Keywords: COVID-19, radiology, respiratory diseases, HRCT

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Authors: Heng Yang, Tao Luo, Yakun Zhang, Kai Wang, Wei Qin, Liang Xie, Ye Yan, Erwei Yin

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Recent years have seen that audio-visual recognition has shown great potential in a strong noise environment. The existing method of audio-visual recognition has explored methods with ResNet and feature fusion. However, on the one hand, ResNet always occupies a large amount of memory resources, restricting the application in engineering. On the other hand, the feature merging also brings some interferences in a high noise environment. In order to solve the problems, we proposed an effective framework with bidirectional distillation. At first, in consideration of the good performance in extracting of features, we chose the light model, Efficientnet as our extractor of spatial features. Secondly, self-distillation was applied to learn more information from raw data. Finally, we proposed a bidirectional distillation in decision-level fusion. In more detail, our experimental results are based on a multi-model dataset from 24 volunteers. Eventually, the lipreading accuracy of our framework was increased by 2.3% compared with existing systems, and our framework made progress in audio-visual fusion in a high noise environment compared with the system of audio recognition without visual.

Keywords: lipreading, audio-visual, Efficientnet, distillation

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Authors: Peter Shi

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Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Amic G. Ho

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Language usage and typography in Hong Kong are unique, as can be seen clearly on the streets of the city. In contrast to many other parts of the world, where there is only one language, in Hong Kong many signs and billboards display two languages: Chinese and English. The language usage on signage, fonts and types used, and the designs in magazines and advertisements all demonstrate the unique features of Hong Kong typographic design, which reflect the multicultural nature of Hong Kong society. This study is the first step in investigating the nature and development of Hong Kong typography. The preliminary research explored how the historical development of Hong Kong is reflected in its unique typography. Following a review of historical development, a quantitative study was designed: Local Hong Kong participants were invited to provide input on what makes the Hong Kong typographic style unique. Their input was collected and analyzed. This provided us with information about the characteristic criteria and features of Hong Kong typography, as recognized by the local people. The most significant typographic designs in Hong Kong were then investigated and the influence of Chinese and other cultures on Hong Kong typography was assessed. The research results provide an indication to local designers on how they can strengthen local design outcomes and promote the values and culture of their mother town.

Keywords: typography, Hong Kong, historical developments, multiple cultures

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Authors: Mabrook Saleh Amera, Prabhakarn Arunachalama, Maged N. Shaddadb, Abdulhadi Al-Qadia

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Global energy crises and water pollution have negatively impacted sustainable development in recent years. It is most promising to use Bismuth vanadate (BiVO4) as an electrode for photoelectrocatalytic (PEC) oxidation of water and pollution degradation. However, BiVO4 anodes suffer from poor charge separation and slow water oxidation. In this paper, a Zr:BiVO4/NiFeOOH heterojunction was successfully prepared by electrodeposition and photoelectrochemical transformation process. The method resulted in a notable 5-fold improvement in photocurrent features (1.27 mAcm−2 at 1.23 VRHE) and a lower onset potential of 0.6 VRHE. Photoanodes with high photocatalytic features and high photocorrosion resistance may be attributed their high conformity and amorphous nature of the coating. In this study, PEC was compared to electrocatalysis (EC), and the effect of bias potential on PEC degradation was discussed for tetracycline (TCH), riboflavin, and streptomycin. In PEC, TCH was degraded in the most efficient way (96 %) by Zr:BiVO4/NiFeOOH, three times larger than Zr:BiVO4 and EC (55 %). Thus, this study offers a potential solution for oxidizing PEC water and treating water pollution.

Keywords: photoelectrochemical, water splitting, pharmaceutical pollutants degradation, photoanodes, cocatalyst

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Authors: Laszlo Dorner

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Background: In the last few decades there has been a significant discussion within the researchers of prosocial behavior about to what extent personality characteristics matter in determining the quality and frequency of helping behaviors. Of these community activities the most important is formal volunteering which mainly realises in civil services and organizations. Recently many researches have been showed up regarding the personality factors and motivations behind volunteering). Most of these researches found strong correlation between Agreeableness and Extraversion as global traits and the time spent on volunteering and its frequency as well. Aims of research: In this research we investigate the relation between formal volunteer activities and global traits in a Hungarian volunteer sample. We hypothetise that the results appeared in the previous researches show the same pattern in Hungary as well: volunteering would be related to Agreeableness and Extraversion. We also assume that the time spent on volunteering is related to these traits, since these traits would serve as an indicator of long-term volunteering. Methods: We applied the Hungarian adaptation of Big Five Questionnaire created by Caprara, Barbaranelli és Borgogni. This self-reported questionnaire contains 132 items, and explore 5 main traits examining the person’s most important emotional and motivational features regarding its personality. This research took into account the most important socio-economical factors (age, gender, religiosity, income) which can determine volunteer activities per se. The data is evaluated by SPSS 19.0 Statistical Software. Sample: 92 volunteer (formal, mainly the volunteers of Hungarian Red Cross and Hospice Organizations)and 92 non volunteer person, with matched subsamples by the factors of age, gender and qualification. Results: The volunteer subsample shows higher values of Energy and significantly higher values of Agreeableness and Openness, however, regarding Conscientiousness and Emotional Stability the differences are not significant between the volunteer and non-volunteer subsamples.

Keywords: Big Five, comparative analysis, global traits, volunteering

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Oluwafemi Samuel Oyamakin

Abstract:

The forest in Nigeria is currently estimated to extend to around 9.6 million hectares, but used to expand over central and southern Nigeria decades ago. The forest estate is shrinking due to long-term human exploitation for agricultural development, fuel wood demand, uncontrolled forest harvesting and urbanization, amongst other factors, compounded by population growth in rural areas. Nigeria has lost more than 50% of its forest cover since 1990 and currently less than 10% of the country is forested. The current deforestation rate is estimated at 3.7%, which is one of the highest in the world. Reducing Emissions from Deforestation and forest Degradation plus conservation, sustainable management of forests and enhancement of forest carbon stocks constituted what is referred to as REDD+. This study evaluated some of the existing way of computing carbon stocks using eight indigenous tree species like Mansonia, Shorea, Bombax, Terminalia superba, Khaya grandifolia, Khaya senegalenses, Pines and Gmelina arborea. While these components are the essential elements of REDD+ programme, they can be brought under a broader framework of systems analysis designed to arrive at optimal solutions for future predictions through statistical distribution pattern of carbon sequestrated by various species of tree. Available data on height and diameter of trees in Ibadan were studied and their respective potentials of carbon sequestration level were assessed and subjected to tests so as to determine the best statistical distribution that would describe the carbon sequestration pattern of trees. The result of this study suggests a reasonable statistical distribution for carbons sequestered in simulation studies and hence, allow planners and government in determining resources forecast for sustainable development especially where experiments with real-life systems are infeasible. Sustainable management of forest can then be achieved by projecting future condition of forests under different management regimes thereby supporting conservation and REDD+ programmes in Nigeria.

Keywords: REDD+, carbon, climate change, height and diameter

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Authors: A. K. Areamu, J. C. Igbeka

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The performance of box-type solar cookers has been reported by several researchers but little attention was paid to the effect of the type of insulation material on the energy and exergy efficiency of these cookers. This research aimed at evaluating the energy and exergy efficiencies of the box-type cookers containing different insulation materials. Energy and exergy efficiencies of five box-type solar cookers insulated with maize cob, air (control), maize husk, coconut coir and polyurethane foam respectively were obtained over a period of three years. The cookers were evaluated using water heating test procedures in determining the energy and exergy analysis. The results were subjected to statistical analysis using ANOVA. The result shows that the average energy input for the five solar cookers were: 245.5, 252.2, 248.7, 241.5 and 245.5J respectively while their respective average energy losses were: 201.2, 212.7, 208.4, 189.1 and 199.8J. The average exergy input for five cookers were: 228.2, 234.4, 231.1, 224.4 and 228.2J respectively while their respective average exergy losses were: 223.4, 230.6, 226.9, 218.9 and 223.0J. The energy and exergy efficiency was highest in the cooker with coconut coir (37.35 and 3.90% respectively) in the first year but was lowest for air (11 and 1.07% respectively) in the third year. Statistical analysis showed significant difference between the energy and exergy efficiencies over the years. These results reiterate the importance of a good insulating material for a box-type solar cooker.

Keywords: efficiency, energy, exergy, heating insolation

Procedia PDF Downloads 353

Authors: Abidi Myriam, Ben Amor Rim, Gueddari Moncef

Abstract:

Understanding ecosystem response to the restoration project is essential to assess its rehabilitation. Indeed, the time elapsed after restoration is a critical indicator to shows the real of the restoration success. In this order, the south lagoon of Tunis, a shallow Mediterranean coastal area, has witnessed several pollutions. To resolve this environmental problem, a large restoration project of the lagoon was undertaken. In this restoration works, the main changes are the decrease of the residence time of the lagoon water and the nutrient concentrations. In this paper, we attempt to evaluate the trophic state of lagoon water for evaluating the risk of eutrophication after almost 16 years of its restoration. To attend this objectives water quality monitoring was untaken. In order to identify and to analyze the natural and anthropogenic factor governing the nutrients concentrations of lagoon water geochemical methods and multivariate statistical tools were used. Results show that nutrients have duel sources due to the discharge of municipal wastewater of Megrine City in the south side of the lagoon. The Carlson index shows that the South lagoon of Tunis Lagoon Tunis is eutrophic, and may show limited summer anoxia.

Keywords: geochemistry, nutrients, statistical analysis, the south lagoon of Tunis, trophic state

Procedia PDF Downloads 173

Authors: Bin Feng, Zhulin Zong

Abstract:

Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

Procedia PDF Downloads 66

Authors: Qi Wang

Abstract:

Chinese has become a powerful worldwide language and millions of learners are studying it all over the words. Chinese is a tone language with unique meaningful characters, which makes foreign learners master it with more difficulties. On the other hand, as each foreign language, the learners of Chinese first will learn the basic Chinese Sound Structure (the initials and finals, tones, Neutral Tone and Tone Sandhi). It’s quite common that in the following studies, teachers made a lot of efforts on drilling and error correcting, in order to help students to pronounce correctly, but ignored the training of suprasegmental features (e.g. stress, intonation). This paper analysed the oral data based on our graduation students (two-year program) from 2006-2013, presents the intonation pattern of our graduates to speak Chinese as second language -high and plain with heavy accents, without lexical stress, appropriate stop endings and intonation, which led to the misunderstanding in different real contexts of communications and the international official Chinese test, e.g. HSK (Chinese Proficiency Test), HSKK (HSK Speaking Test). This paper also demonstrated how the Chinese to use the suprasegmental features strategically in different functions and moods (declarative, interrogative, imperative, exclamatory and rhetorical intonations) in order to train the learners to achieve better Communicative Performance.

Keywords: second language learning, suprasegmental, communication, HSK (Chinese Proficiency Test)

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Authors: Sadia Basar, Naila Habib, Awais Adnan

Abstract:

The paper presents the Technique for Image Segmentation Using K-Means Clustering Classification. The presented algorithms were specific, however, missed the neighboring information and required high-speed computerized machines to run the segmentation algorithms. Clustering is the process of partitioning a group of data points into a small number of clusters. The proposed method is content-aware and feature extraction method which is able to run on low-end computerized machines, simple algorithm, required low-quality streaming, efficient and used for security purpose. It has the capability to highlight the boundary and the object. At first, the user enters the data in the representation of the input. Then in the next step, the digital image is converted into groups clusters. Clusters are divided into many regions. The same categories with same features of clusters are assembled within a group and different clusters are placed in other groups. Finally, the clusters are combined with respect to similar features and then represented in the form of segments. The clustered image depicts the clear representation of the digital image in order to highlight the regions and boundaries of the image. At last, the final image is presented in the form of segments. All colors of the image are separated in clusters.

Keywords: clustering, image segmentation, K-means function, local and global minimum, region

Procedia PDF Downloads 359

Authors: Areej Al-Khabbaz, Swapna Goerge, Majedah Abdul-Rasoul

Abstract:

Introduction: The most prevalent periodontal disease among children is gingivitis, and it usually becomes more severe in adolescence. A number of intervention studies suggested that resolution of periodontal inflammation can improve metabolic control in patients diagnosed with diabetes mellitus. Aim: to assess the effect of non-surgical periodontal therapy on glycemic control of children diagnosed with diabetes mellitus. Method: Twenty-eight children diagnosed with diabetes mellitus were recruited with established diagnosis diabetes for at least 1 year. Informed consent and child assent form were obtained from children and parents prior to enrolment. The dental examination for the participants was performed on the same week directly following their annual medical assessment. All patients had their glycosylated hemoglobin (HbA1c%) test one week prior to their annual medical and dental visit and 3 months following non-surgical periodontal therapy. All patients received a comprehensive periodontal examination The periodontal assessment included clinical attachment loss, bleeding on probing, plaque score, plaque index and gingival index. All patients were referred for non-surgical periodontal therapy, which included oral hygiene instruction and motivation followed by supra-gingival and subg-ingival scaling using ultrasonic and hand instruments. Statistical Analysis: Data were entered and analyzed using the Statistical Package for Social Science software (SPSS, Chicago, USA), version 18. Statistical analysis of clinical findings was performed to detect differences between the two groups in term of periodontal findings and HbA1c%. Binary logistic regression analysis was performed in order to examine which factors were significant in multivariate analysis after adjusting for confounding between effects. The regression model used the dependent variable ‘Improved glycemic control’, and the independent variables entered in the model were plaque index, gingival index, bleeding %, plaque Statistical significance was set at p < 0.05. Result: A total of 28 children. The mean age of the participants was 13.3±1.92 years. The study participants were divided into two groups; Compliant group (received dental scaling) and non-complaints group (received oral hygiene instructions only). No statistical difference was found between compliant and non-compliant group in age, gender distribution, oral hygiene practice and the level of diabetes control. There was a significant difference between compliant and non-compliant group in term of improvement of HBa1c before and after periodontal therapy. Mean gingival index was the only significant variable associated with improved glycemic control level. In conclusion, this study has demonstrated that non-surgical mechanical periodontal therapy can improve HbA1c% control. The result of this study confirmed that children with diabetes mellitus who are compliant to dental care and have routine professional scaling may have better metabolic control compared to diabetic children who are erratic with dental care.

Keywords: children, diabetes, metabolic control, periodontal therapy

Procedia PDF Downloads 142