Search results for: Features of Bitcoin

Authors: Lanchi Xie, Zhihui Li, Zhigang Li, Guiqiang Wang, Lei Xu, Yuwen Yan

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Image-based facial features can be classified into category recognition features and individual recognition features. Current automated face recognition systems extract a specific feature vector of different dimensions from a facial image according to their pre-trained neural network. However, to improve the efficiency of parameter calculation, an algorithm generally reduces the image details by pooling. The operation will overlook the details concerned much by forensic experts. In our experiment, we adopted a variety of face recognition algorithms based on deep learning, compared a large number of naturally collected face images with the known data of the same person's frontal ID photos. Downscaling and manual handling were performed on the testing images. The results supported that the facial recognition algorithms based on deep learning detected structural and morphological information and rarely focused on specific markers such as stains and moles. Overall performance, distribution of genuine scores and impostor scores, and likelihood ratios were tested to evaluate the accuracy of biometric systems and forensic experts. Experiments showed that the biometric systems were skilled in distinguishing category features, and forensic experts were better at discovering the individual features of human faces. In the proposed approach, a fusion was performed at the score level. At the specified false accept rate, the framework achieved a lower false reject rate. This paper contributes to improving the interpretability of the objective method of facial comparison and provides a novel method for human-machine collaboration in this field.

Keywords: likelihood ratio, automated facial recognition, facial comparison, biometrics

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Authors: C. V. Aravinda, H. N. Prakash

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In this paper, we tried to convey fusion and state of art pertaining to SIL character recognition systems. In the first step, the text is preprocessed and normalized to perform the text identification correctly. The second step involves extracting relevant and informative features. The third step implements the classification decision. The three stages which involved are Data acquisition and preprocessing, Feature extraction, and Classification. Here we concentrated on two techniques to obtain features, Feature Extraction & Feature Selection. Edge-hinge distribution is a feature that characterizes the changes in direction of a script stroke in handwritten text. The edge-hinge distribution is extracted by means of a windowpane that is slid over an edge-detected binary handwriting image. Whenever the mid pixel of the window is on, the two edge fragments (i.e. connected sequences of pixels) emerging from this mid pixel are measured. Their directions are measured and stored as pairs. A joint probability distribution is obtained from a large sample of such pairs. Despite continuous effort, handwriting identification remains a challenging issue, due to different approaches use different varieties of features, having different. Therefore, our study will focus on handwriting recognition based on feature selection to simplify features extracting task, optimize classification system complexity, reduce running time and improve the classification accuracy.

Keywords: word segmentation and recognition, character recognition, optical character recognition, hand written character recognition, South Indian languages

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Authors: Zebiniso Mukhsinova

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This article discusses the dynamics of the historical development of the Bukhara school of copper-stamped products. Copper embossing is one of the leading crafts of Uzbek decorative and applied art. A critical and analytical assessment of innovative ideas, artistic and technological features, which arose as a result of the inter-regional synthesis of a local school, is presented. The article includes a detailed analysis of exhibits in museum collections, a research of the scientific papers of leading art critics and differs from previous studies in this area.

Keywords: applied art, copper embossing, metalwork, ewer, tray, Bukhara school

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Authors: Tomas Trainys, Algimantas Venckauskas

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Application of biometric features to the cryptography for human identification and authentication is widely studied and promising area of the development of high-reliability cryptosystems. Biometric cryptosystems typically are designed for patterns recognition, which allows biometric data acquisition from an individual, extracts feature sets, compares the feature set against the set stored in the vault and gives a result of the comparison. Preprocessing and fusion of biometric data are the most important phases in generating a feature vector for key generation or authentication. Fusion of biometric features is critical for achieving a higher level of security and prevents from possible spoofing attacks. The paper focuses on the tasks of initial processing and fusion of multiple representations of finger vein modality patterns. These tasks are solved by applying conventional image preprocessing methods and machine learning techniques, Convolutional Neural Network (SVM) method for image segmentation and feature extraction. An article presents a method for generating sets of biometric features from a finger vein network using several instances of the same modality. Extracted features sets were fused at the feature level. The proposed method was tested and compared with the performance and accuracy results of other authors.

Keywords: bio-cryptography, biometrics, cryptographic key generation, data fusion, information security, SVM, pattern recognition, finger vein method.

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Shanxiong Chen, Xu Han, Xiaolong Wang, Hui Ma

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Yi is an ethnic group mainly living in mainland China, with its own spoken and written language systems, after development of thousands of years. Ancient Yi is one of the six ancient languages in the world, which keeps a record of the history of the Yi people and offers documents valuable for research into human civilization. Recognition of the characters in ancient Yi helps to transform the documents into an electronic form, making their storage and spreading convenient. Due to historical and regional limitations, research on recognition of ancient characters is still inadequate. Thus, deep learning technology was applied to the recognition of such characters. Five models were developed on the basis of the four-layer convolutional neural network (CNN). Alpha-Beta divergence was taken as a penalty term to re-encode output neurons of the five models. Two fully connected layers fulfilled the compression of the features. Finally, at the softmax layer, the orthographic features of ancient Yi characters were re-evaluated, their probability distributions were obtained, and characters with features of the highest probability were recognized. Tests conducted show that the method has achieved higher precision compared with the traditional CNN model for handwriting recognition of the ancient Yi.

Keywords: recognition, CNN, Yi character, divergence

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Authors: Lin Huo, Xianwei Liu, Jingxiong Zhou

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Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective.

Keywords: image auto-annotation, color correlograms, Hash code, image retrieval

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, seizure, phase correlation, fluctuation, deviation.

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Authors: Emine Yılmaz Bolat

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Early childhood is a very important period in terms of identifying and developing early skills and abilities. It is likely that the child's development will be in the same direction in the future. This study was conducted with 26 children for the purpose of examining pictures of children of five years old. In the survey, children were asked to draw a picture with pastel dyes. The drawings were collected and evaluated by the researcher. At the end of the research, it was found that the children used the yellow color (N = 17, 16,34%) and the least gray color (N = 1, 0,96%). When the features of children's pictures are examined, the children's paintings have been found to have hierarchy, transparency, completion, the use of vivid colors, and the presence of vertical and horizontal painting lines.

Keywords: early childhood, kindergarten, pictures of children, features of pictures

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Authors: Pitimanus Bunlue

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The objectives of this study were to identify the unique characteristics of Salaya Old market, Phutthamonthon, Nakhon Pathom and develop the effective video media to promote the homeland awareness among local people and the characteristic features of this community were collectively summarized based on historical data, community observation, and people’s interview. The acquired data were used to develop a media describing prominent features of the community. The quality of the media was later assessed by interviewing local people in the old market in terms of content accuracy, video, and narration qualities, and sense of homeland awareness after watching the video. The result shows a 6-minute video media containing historical data and outstanding features of this community was developed. Based on the interview, the content accuracy was good. The picture quality and the narration were very good. Most people developed a sense of homeland awareness after watching the video also as well.

Keywords: audio-visual, creating homeland awareness, Phutthamonthon Nakhon Pathom, research and development

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

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Authors: Saira Soroya, Khalid Mahmood

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Library catalogs open the door to the library collection. OPAC (Online Public Access Catalog) are one of the services offered by automated libraries. The present study aims to explore user’s awareness, the level of use and their searching behavior of OPAC with a purpose to give suggestions and ways to improve user-friendly features of library OPAC. The population consisted of OPAC users of Lahore University of Management Sciences (LUMS). Convenient sampling technique was carried out. Total sample size was 100 OPAC users. Quantitative research design, based on survey method used to carry out the study. The data collection instrument was adopted. Data was analyzed using SPSS. Results revealed that a considerable number of users were not aware of OPAC i.e. (30%); however, those who were aware were using basic features of the OPAC. It was found that lack of knowledge was considered the frequent reason for not using all features of OPAC. In this regard, it is strongly recommended that compulsory information literacy programme should be established.

Keywords: catalog, OPAC, library automation, usability study, university library

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Authors: Michael Zimba

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A duplicated image region may be subjected to a number of attacks such as noise addition, compression, reflection, rotation, and scaling with the intention of either merely mating it to its targeted neighborhood or preventing its detection. In this paper, we present an effective and robust method of detecting duplicated regions inclusive of those affected by the various attacks. In order to reduce the dimension of the image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of a suspicious image. However, unlike most existing copy move image forgery (CMIF) detection algorithms operating in the DWT domain which extract only the low frequency sub-band of the DWT of the suspicious image thereby leaving valuable information in the other three sub-bands, the proposed algorithm simultaneously extracts features from all the four sub-bands. The extracted features are not only more accurate representation of image regions but also robust to additive noise, JPEG compression, and affine transformation. Furthermore, principal component analysis-eigenvalue decomposition, PCA-EVD, is applied to reduce the dimension of the features. The extracted features are then sorted using the more computationally efficient Radix Sort algorithm. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. The proposed algorithm is not only fast but also more robust to attacks compared to the related CMIF detection algorithms. The experimental results show high detection rates.

Keywords: affine transformation, discrete wavelet transform, radix sort, SATS

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Authors: Bong Borero Lumabao

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This qualitative research is a study on the lines of Fliptop talks performed by the Fliptop rappers employing Finnegan’s (2008) discourse analysis. This paper aimed to analyze the phonological, morphological, and semantic features of the fliptop talk, to explore the structures in the lines of Fliptop among Filipino youth, and to uncover the various insights that can be gained from it. The corpora of the study included all the 20 Fliptop Videos downloaded from the Youtube Channel of Fliptop. Results revealed that Fliptop contains phonological features such as assonance, consonance, deletion, lengthening, and rhyming. Morphological features include acronym, affixation, blending, borrowing, code-mixing and switching, compounding, conversion or functional shifts, and dysphemism. Semantics presented the lexical category, meaning, and words used in the fliptop talks. Structure of Fliptop revolves on the personal attack (physical attributes), attack on the bars (rapping skills), extension: family members and friends, antithesis, profane words, figurative languages, sexual undertones, anime characters, homosexuality, and famous celebrities involvement.

Keywords: discourse analysis, fliptop talks, filipino youth, fliptop videos, Philippines

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Authors: Faraz Tahir, M. Usman Akram, Albab Ahmad Khan, Mujahid Abbass, Ahmad Tariq, Nuzhat Qaiser

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The objective of this study was to evaluate the Degree of anemia by taking the picture of the palpebral conjunctiva using Smartphone Camera. We have first localized the region of interest from the image and then extracted certain features from that Region of interest and trained SVM classifier on those features and then, as a result, our system classifies the image in real-time on their level of hemoglobin. The proposed system has given an accuracy of 70%. We have trained our classifier on a locally gathered dataset of 30 patients.

Keywords: anemia, palpebral conjunctiva, SVM, smartphone

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Authors: Jessevel Montes

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This study explored on the culture of journal writing among the Senior High School Manobo students. The purpose of this qualitative morpho-semantic and syntactic study was to discover the morphological, semantic, and syntactic features of the written output through morphological, semantic, and syntactic categories present in their journal writings. Also, beliefs and practices embedded in the norms, values, and ideologies were identified. The study was conducted among the Manobo students in the Senior High Schools of Central Mindanao, particularly in the Division of North Cotabato. Findings revealed that morphologically, the features that flourished are the following: subject-verb concordance, tenses, pronouns, prepositions, articles, and the use of adjectives. Semantically, the features are the following: word choice, idiomatic expression, borrowing, and vernacular. Syntactically, the features are the types of sentences according to structure and function; and the dominance of code switching and run-on sentences. Lastly, as to the beliefs and practices embedded in the norms, values, and ideologies of their journal writing, the major themes are: valuing education, family, and friends as treasure, preservation of culture, and emancipation from the bondage of poverty. This study has shed light on the writing capabilities and weaknesses of the Manobo students when it comes to English language. Further, such an insight into language learning problems is useful to teachers because it provides information on common trouble-spots in language learning, which can be used in the preparation of effective teaching materials.

Keywords: applied linguistics, culture, morpho-semantic and syntactic analysis, Manobo Senior High School, Philippines

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Authors: Orlyn Joyce D. Esquivel

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This study analyzed 50 selected research papers from professional language and linguistic academic journals to portray the differences between Kachru’s (1994) outer circle and expanding circle Englishes. The selected outer circle Englishes include those of Bangladesh, Malaysia, the Philippines, India, and Singapore; and the selected expanding circle Englishes are those of China, Indonesia, Japan, Korea, and Thailand. The researcher built ten corpora (five research papers for each corpus) to represent each variety of Englishes. The corpora were examined under grammatical and lexical features using Modified English TreeTagger in Sketch Engine. Results revealed the distinct grammatical and lexical features through the table and textual analyses, illustrated from the most to least dominant linguistic elements. In addition, comparative analyses were done to distinguish the features of each of the selected Englishes. The Language Change Theory was used as a basis in the discussion. Hence, the findings suggest that the ‘outer circle’ Englishes and ‘expanding circle’ Englishes will continue to drift from International English.

Keywords: applied linguistics, English as a global language, expanding circle Englishes, global Englishes, outer circle Englishes

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Soumya Sajjan

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Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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

Abstract:

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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