Search results for: Korean linguistic feature

Authors: Polycarp A. Igbojekwe, Chizoba Amajuoyi, Peterson Nwokorie

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The main purpose of this study was to carry out a survey of customer’s aggression towards hotel workers and make contributions on the prevalence and rationale behind customer’s aggression. Data for the study were gathered with a four-point Likert type rating scale. Samples were drawn from frontline hotel employees, managers and customers of twelve (12) hotels selected from three zones of Imo State. Data analyses were conducted using simple percentage, descriptive statistics; and Z-test statistical technique was used to test hypotheses. Among other factors, service failure and verbal abuse by service providers and poor quality product compared to price were identified by customers as the three major factors that can lead to customer aggression. Frontline employees indentified verbal abuse as the most common mode of aggression and that customer aggression causes emotional disturbance in them. The study also revealed that customer aggression is more prevalent in the 1&2 star hotels than it is in 3-5 star hotels. Most of the hotels have not institutionalized systematic approaches needed to effectively face the challenges of customer aggression, thus, customer aggression has become a common feature in the industry. Frontline jobs demand high emotional input. Therefore, we recommend that frontline employees should be given emotional support by their managers and also trained on how to cope with emotional disturbance.

Keywords: customer aggression, emotional disturbance, employee well-being, service failure, verbal abuse

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Authors: Edilei Reis, Jose Carlos Felix

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This paper explores Waldir Dupont’s translations of William Faulkner’s novels to Brazilian Portuguese language in order to comprehend how his translation project regarding Faulkner’s works has addressed modernist traits of the novelist fiction, particularly the ambivalence of language, multiple and fragmented points of view and syntax. Wladir Dupont (1939-2014) was a prolific Brazilian journalist who benefitted from his experiences as an international correspondent living abroad (EUA and Mexico) to become an acclaimed translator later in life. He received a Jabuiti Award (Brazilian most prestigious literary award) for his translation of ‘La Otra Voz’ (1994), by Mexican poet, critic and translator Octavio Paz, a writer to whom he devoted the first years of his carrier as a translator. As Dupont pointed out in some interviews, the struggles in finding a way out to overcome linguistic and cultural obstacles in the process of translating texts from Spanish to Portuguese was paramount for ascertaining his engagement in the long-term project of translating to Brazilian Portuguese the fiction of William Faulkner. His first enterprise was the translation of Faulkner’s trilogy Snopes: The Hamlet (1940) and The Town (1957), the first two novels, were published in 1997 as O povoado and A cidade; in 1999 the last novel, The mansion (1959), was published as A mansão. In 2001, Dupont tackled what is considered one of the most challenging novels by the author due to his use of multiple points of view, As I lay dying (1930). In 2003, The Reivers (1962) was published under the title Os invictos. His enterprise finishes in 2012 with the publication of an anthology of Faulkner’s thriller short-stories Knight’s Gambit (1932) as Lance mortal. Hence, in this paper we will consider the Dupont’s trajectory as a translator, paying special attention to the way in which his identity as such is constituted through the process of translating Faulkner’s works.

Keywords: literary translation, translator’s identity, William Faulkner, Wladir DuPont

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Authors: Smoki Musaraj

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Classic studies of the Mediterranean as a cultural and geographic unit of analysis have emphasized the theme of cosmopolitan urbanism as a key feature of the Mediterranean city. This paper explores the Mediterranean city today, considering continuities and ruptures from images of the Mediterranean of the past. The paper seeks to address the following questions: What are some defining characteristics of Mediterranean cities today? What are some of the shared challenges? The paper focuses on two interrelated themes: public space and tourism management. Several examples of protest and contestation in Mediterranean cities will be analyzed. These examples include cities where tourism presents opportunities and challenges to city planning and management; and where new private and public developments threaten the management of public space. The paper draws on ethnographic research in the city of Saranda, Albania, a small attractive tourist destination on the border with Greece, and Barcelona, Spain, a leading example of urban transformation and tourism massification. While different in size and popularity, both cities share some similar developments and contestations. In both cities, authorities have taken up different strategies to manage tourism and restore public space. The comparison will focus on social movements in the respective cities that target tourism and urban development in the name of preserving theirMediterraneaness. These examples are used to reflect more broadly on what are some features of the Mediterranean city today and how they can be preserved in the current climate of tourism expansion of urban development boom.

Keywords: mediterranean, urbanism, tourism, public space, anthropology, human geography, sustainability

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Authors: Alexandra-Monica Toma

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Internet memes are an element of computer mediated communication and an important part of online culture that combines text and image in order to generate meaning. This term coined by Richard Dawkings refers to more than a mere way to briefly communicate ideas or emotions, thus naming a complex and an intensely perpetuated phenomenon in the virtual environment. This paper approaches memes as a cultural artefact and a virtual trope that mirrors societal concerns and issues, and analyses the pragmatics of their use. Memes have to be analysed in series, usually relating to some image macros, which is proof of the interplay between imitation and creativity in the memes’ writing process. We believe that their potential to become viral relates to three key elements: adaptation to context, reference to a successful meme series, and humour (jokes, irony, sarcasm), with various pragmatic functions. The study also uses the concept of multimodality and stresses how the memes’ text interacts with the image, discussing three types of relations: symmetry, amplification, and contradiction. Moreover, the paper proves that memes could be employed as speech acts with illocutionary force, when the interaction between text and image is enriched through the connection to a specific situation. The features mentioned above are analysed in a corpus that consists of memes related to the COVID-19 pandemic. This corpus shows them to be highly adaptable to context, which helps build the feeling of connection and belonging in an otherwise tremendously fragmented world. Some of them are created based on well-known image macros, and their humour results from an intricate dialogue between texts and contexts. Memes created in relation to the COVID-19 pandemic can be considered speech acts and are often used as such, as proven in the paper. Consequently, this paper tackles the key features of memes, makes a thorough analysis of the memes sociocultural, linguistic, and situational context, and emphasizes their intertextuality, with special accent on their illocutionary potential.

Keywords: context, memes, multimodality, speech acts

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Guorong Sui, Xingwei Jia, Fei Tong, Xiumin Gao

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Camera calibration is a fundamental issue of high precision noncontact measurement. And it is necessary to analyze and study the reliability and application range of its linear model which is often used in the camera calibration. According to the imaging features of monocular cameras, a camera model which is based on the image pixel coordinates and three dimensional space coordinates is built. Using our own customized template, the image pixel coordinate is obtained by the subpixel corner detection method. Without considering the aberration of the optical system, the feature extraction and linearity analysis of the line segment in the template are performed. Moreover, the experiment is repeated 11 times by constantly varying the measuring distance. At last, the linearity of the camera is achieved by fitting 11 groups of data. The camera model measurement results show that the relative error does not exceed 1%, and the repeated measurement error is not more than 0.1 mm magnitude. Meanwhile, it is found that the model has some measurement differences in the different region and object distance. The experiment results show this linear model is simple and practical, and have good linearity within a certain object distance. These experiment results provide a powerful basis for establishment of the linear model of camera. These works will have potential value to the actual engineering measurement.

Keywords: camera linear model, geometric imaging relationship, image pixel coordinates, three dimensional space coordinates, sub-pixel corner detection

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Authors: Nádia Pereira, Paula Ferreira, Sofia Francisco, Sofia Oliveira, Sidclay Souza, Paula Paulino, Ana Margarida Veiga Simão

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Social media has progressed into a platform for hate speech among its users, and thus, there is an increasing need to develop automatic detection classifiers of offense and conflicts to help decrease the prevalence of such incidents. Online communication can be used to intentionally harm someone, which is why such classifiers could be essential in social networks. A possible application of these classifiers is the automatic detection of cyberbullying. Even though identifying the aggressive language used in online interactions could be important to build cyberbullying datasets, there are other criteria that must be considered. Being able to capture the language, which is indicative of the intent to harm others in a specific context of online interaction is fundamental. Offense and hate speech may be the foundation of online conflicts, which have become commonly used in social media and are an emergent research focus in machine learning and natural language processing. This study presents two Portuguese language offense-related datasets which serve as examples for future research and extend the study of the topic. The first is similar to other offense detection related datasets and is entitled Aggressiveness dataset. The second is a novelty because of the use of the history of the interaction between users and is entitled the Conflicts/Attacks dataset. Both datasets were developed in different phases. Firstly, we performed a content analysis of verbal aggression witnessed by adolescents in situations of cyberbullying. Secondly, we computed frequency analyses from the previous phase to gather lexical and linguistic cues used to identify potentially aggressive conflicts and attacks which were posted on Twitter. Thirdly, thorough annotation of real tweets was performed byindependent postgraduate educational psychologists with experience in cyberbullying research. Lastly, we benchmarked these datasets with other machine learning classifiers.

Keywords: aggression, classifiers, cyberbullying, datasets, hate speech, machine learning

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Authors: Jia Liu

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This article explores the shifting nature of physician-patient relationship in mainland China. Specifically, it takes the physician-patient relationship during the barefoot doctor program in 1968-1978, the marketization of healthcare services in 1978-2002, and the healthcare reform in 2003-2020 as three typical historical periods, illustrating how the nature of the physician-patient relationship has changed over time in mainland China. Drawing on recent jurisprudential literature that emphasizes the roles and functions done by and through obligation rather than right, it explores how the obligations of physicians and patients along with the implementation of informed consent, marketization of the healthcare system, and the basic healthcare reform have affected their relationship. One key feature of this article is that it analyzes the ways in which commodification and decommodification of healthcare have defined and in many different ways have determined the expectations and practices of physicians and patients, which illustrates how the trust between physicians and patients threatens to collapse and the bond between the citizen and the state fails to be firmly established in the mainland Chinese healthcare context. It also pays special attention to the role played by law and legal institutions—for instance, the implementation of informed consent and the liability law—in being complicit in facilitating the decoupling of the practices of physicians and patients from their ethical senses of obligation and undermining the bond (the trust relationship) between them.

Keywords: healthcare, marketization, physician-patient relationship, sense of obligation

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Authors: Hadjer Chellia

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the present study reports on second language use among Algerian international students in the UK. In Algeria, French has an important status among the Algerian verbal repertoires due to colonial reasons. This has triggered many language conflicts and many debates among policy makers in Algeria. In higher education, Algerian English students’ sociolinguistic profile is characterised by the use of French as a sign of prestige. What may leave room for debate is the effect of crossing borders towards the UK as a result of international mobility programmes, a transition which could add more complexity since French, is not so significant as a language in the UK context. In this respect, the micro-objective is to explore the fate of French use among Ph.D. students in the UK as a newly established group vis-à-vis English. To fulfill the purpose of the present inquiry, the research employs multiple approaches in which semi-structured interview is a primary source of data to know participants’ attitudes about French use, targeting both their pre-migratory experience and current one. Web-based questionnaires are set up to access larger population. Focus group sessions are further procedures of scrutiny in this piece of work to explore the actual linguistic behaviours. Preliminary findings from both interviews and questionnaires reveal that students’ current experience, particularly living in the UK, affects their pre-migratory attitudes towards French language and its use. The overall findings are expected to bring manifold contributions to the field of research among which is setting factors that influence language use among newly established émigrés communities. The research is also relevant to international students’ experience of study abroad in terms of language use in the guise of internationalization of higher education, mobility and exchange programmes. It could contribute to the sociolinguistics of the Algerian diaspora: the dispersed residence of non-native communities - not to mention its significance on the Algerian research field abroad.

Keywords: Algerian diaspora, French language, language maintenance, language shift, mobility

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Authors: Amr AbdelAzim Ali, G. A. Elsheikh, Moutaz M. Hegazy

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Reaction wheels (RWs) are generally used as main actuator in the attitude control system (ACS) of spacecraft (SC) for fast orientation and high pointing accuracy. In order to achieve the required accuracy for the RWs model, the main characteristics of the RWs that necessitate analysis during the ACS design phase include: technical features, sequence of operating and RW control logic are included in function (behavior) model. A mathematical model is developed including the various errors source. The errors in control torque including relative, absolute, and error due to time delay. While the errors in angular velocity due to differences between average and real speed, resolution error, loose in installation of angular sensor, and synchronization errors. The friction torque is presented in the model include the different feature of friction phenomena: steady velocity friction, static friction and break-away torque, and frictional lag. The model response is compared with the experimental torque and frequency-response characteristics of tested RWs. Based on the created RW model, some criteria of optimization based control torque allocation problem can be recommended like: avoiding the zero speed crossing, bias angular velocity, or preventing wheel from running on the same angular velocity.

Keywords: friction torque, reaction wheels modeling, software in the loop, spacecraft attitude control

Procedia PDF Downloads 259

Authors: Mohamed Hamed

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Population studies, essentially deals with the size, growth, and distribution of the population in a given area. Size, growth, and distribution are determined by three major factors, which are reproduction, mortality, and migration. Of these factors, reproduction is a potent socio-demographic force in vital process of population growth. It is a major component of population growth, and has crucial role in population dynamic, because it measures the rate at which a population increased. In fact the most striking feature of human reproduction is its variation. Its levels are vary widely among nations, countries, geographic regions, ethnic. The variations of reproductive behaviour among married women have been empirically documented in a large numbers of countries. For instance, many researchers in developing and developed countries investigated the differential of reproductive behaviour among married women. Most of these studies found that reproductive behaviour is strongly influenced by the socioeconomic and biological factors.Such as education, income, employment of women, marriage pattern, age at marriage, contraceptive use, education, and employment. However, the above socioeconomic and biological factors are determined by cultural factors surrounded by married women. So, this study is going to find out the effect of culture on reproductive behaviour among married women in Sudan, a case of Shendi town.

Keywords: fertilty pattern, sudan, shendi town, factors affecting reproductive behaviour, married women

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Authors: Benga Ebouele, Thomas Tengen

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Implementing either periodic or continuous inventory review model within most manufacturing-companies-supply chains as a management tool may incur higher costs. These high costs affect the system flexibility which in turn affects the level of service required to satisfy customers. However, these effects are not clearly understood because the parameters of both inventory review policies (protection demand interval, order quantity, etc.) are not designed to be fully utilized under different and uncertain conditions such as poor manufacturing, supplies and delivery performance. Coming up with a hybrid model which may combine in some sense the feature of both continuous and a periodic inventory review models should be useful. Therefore, there is a need to build and evaluate such hybrid model on the annual total cost, stock out probability and system’s flexibility in order to search for the most cost effective inventory review model. This work also seeks to find the optimal sets of parameters of inventory management under stochastic condition so as to optimise each policy independently. The results reveal that a continuous inventory system always incurs lesser cost than a periodic (R, S) inventory system, but this difference tends to decrease as time goes by. Although the hybrid inventory is the only one that can yield lesser cost over time, it is not always desirable but also natural to use it in order to help the system to meet high performance specification.

Keywords: demand and lead time randomness, hybrid Inventory model, optimization, supply chain

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Authors: Xinyuan Y. Song, Kai Kang

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Joint models for analyzing longitudinal and survival data are widely used to investigate the relationship between a failure time process and time-variant predictors. A common assumption in conventional joint models in the survival analysis literature is that all predictors are observable. However, this assumption may not always be supported because unobservable traits, namely, latent variables, which are indirectly observable and should be measured through multiple observed variables, are commonly encountered in the medical, behavioral, and financial research settings. In this study, a joint modeling approach to deal with this feature is proposed. The proposed model comprises three parts. The first part is a dynamic factor analysis model for characterizing latent variables through multiple observed indicators over time. The second part is a random coefficient trajectory model for describing the individual trajectories of latent variables. The third part is a proportional hazard model for examining the effects of time-invariant predictors and the longitudinal trajectories of time-variant latent risk factors on hazards of interest. A Bayesian approach coupled with a Markov chain Monte Carlo algorithm to perform statistical inference. An application of the proposed joint model to a study on the Alzheimer's disease neuroimaging Initiative is presented.

Keywords: Bayesian analysis, joint model, longitudinal data, time-to-event data

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: R. M. Krishna Sureddi, V. Kamakshi Prasad, R. Santosh

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Machine learning techniques are applied for the analysis of the impact of mental illness on the burden of disease. It is calculated using the disability-adjusted life year (DALY). One DALY represents the loss of the equivalent of one year of full health. DALYs for a disease or health condition are the sum of years of life lost due to premature mortality (YLLs) and years of healthy life lost due to disability (YLDs) due to prevalent cases of the disease or health condition in a population. The critical analysis is done based on the Data sources, machine learning techniques and feature extraction method. The reviewing is done based on major databases. The extracted data is examined using statistical analysis and machine learning techniques were applied. The prediction of the impact of mental illness on the population using machine learning techniques is an alternative approach to the old traditional strategies, which are time-consuming and may not be reliable. The approach makes it necessary for a comprehensive adoption, innovative algorithms, and an understanding of the limitations and challenges. The obtained prediction is a way of understanding the underlying impact of mental illness on the health of the people and it enables us to get a healthy life expectancy. The growing impact of mental illness and the challenges associated with the detection and treatment of mental disorders make it necessary for us to understand the complete effect of it on the majority of the population.

Keywords: ML, DALY, BD, DL

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Authors: Anthony Proschka, Deepak Mishra, Merlyn Ramanan, Zurab Baratashvili

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Small and medium-sized businesses receive over 160 billion invoices every year. Since these documents exhibit many subtle differences in layout and text, extracting structured fields such as sender name, amount, and VAT rate from them automatically is an open research question. In this paper, existing work in template-based document extraction is extended, and a system is devised that is able to reliably extract all required fields for up to 70% of all documents in the data set, more than any other previously reported method. The approaches are described for 1) detecting through visual features which template a given document belongs to, 2) automatically generating extraction rules for a given new template by composing regular expressions from multiple components, and 3) computing confidence scores that indicate the accuracy of the automatic extractions. The system can generate templates with as little as one training sample and only requires the ground truth field values instead of detailed annotations such as bounding boxes that are hard to obtain. The system is deployed and used inside a commercial accounting software.

Keywords: data mining, information retrieval, business, feature extraction, layout, business data processing, document handling, end-user trained information extraction, document archiving, scanned business documents, automated document processing, F1-measure, commercial accounting software

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Authors: Piyush Bajpai, Sneha Pandey

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Mumbai’s Dhobighat, 150 year old largest open laundry in the world, is the true live-work place and only source of income for some of Mumbai’s highest density ‘urban poor’ residents. The regeneration of Dhobighat, due to its ultra prime location and complex socio-political culture has been a complex issue. This once flourishing urban industrial core has been degrading for the past several decades mainly due to the decline of the open laundry business, the site’s over burdened infrastructure and conflicting socio-political and economic forces. The phenomena of ‘exaptation’ or ‘co-option’ has been observed by evolutionary biologists as a process responsible for producing highly tenacious and resilient offsprings within a species. The reddish egret uses its wings to cast shadow in shallow waters to attract small fish and hunt them. An unrelated feature used opportunistically to produce a very favorable result. How can this idea of co-option be applied to resolve the complex issue of Dhobighat’s regeneration? Our paper proposes a new methodology/approach for the regeneration of Dhobighat through the lens of evolutionary biology. Forces and systems (social, political, economic, cultural and ecological) that seem conflicting or unrelated by nature are opportunistically transformed into symbiotic and complimentary relationships that produce an inclusive, resilient and holistic solution for the regeneration of Dhobighat.

Keywords: urban regeneration, exaptation, resilience, Dhobighat, Mumbai

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Authors: Amenador Kate Benedicta, Wang Zhiwei

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Since the later parts of the 20th century, the notion of equivalence continues to be a central and critical concept in the development of translation theory. After decades of arguments over word-for-word and free translations methods, scholars attempting to develop more systematic and efficient translation theories began to focus on fundamental translation concepts such as equivalence. Although the concept of equivalence has piqued the interest of many scholars, its definition, scope, and applicability have sparked contentious arguments within the discipline. As a result, several distinct theories and explanations on the concept of equivalence have been put forward over the last half-century. Thus, this study explores and discusses the evolution of the critical concept of equivalence in translation studies through a bibliometric method of investigation of manual and digital books and articles by analyzing different scholars' key contributions and limitations on equivalence from various theoretical perspectives. While analyzing them, emphasis is placed on the innovations that each theory has brought to the comprehension of equivalence. In order to achieve the aim of the study, the article began by discussing the contributions of linguistically motivated theories to the notion of equivalence in translation, followed by functionalist-oriented contributions, before moving on to more recent advancements in translation studies on the concept. Because equivalence is such a broad notion, it is impossible to discuss each researcher in depth. As a result, the most well-known names and their equivalent theories are compared and contrasted in this research. The study emphasizes the developmental progression in our comprehension of the equivalence concept and equivalent effect. It concluded that the various theoretical perspective's contributions to the notion of equivalence rather complement and make up for the limitations of each other. The study also highlighted how troublesome the equivalent concept might become in terms of identifying the nature of translation and how central and unavoidable the concept is in every translation action, despite its limitations. The significance of the study lies in its synthesis of the different contributions and limitations of the various theories offered by scholars on the notion of equivalence, lending literature to both student and scholars in the field, and providing insight on future theoretical development

Keywords: equivalence, functionalist translation theories, linguistic translation approaches, translation theories, Skopos

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Authors: Indu ray

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In today’s digital world the internet is already flooded with information on how to master grammar in a foreign language. It is well known that one cannot master a language without grammar. Grammar is the backbone of any language. Without grammar there would be no structure to help you speak/write or listen/read. Successful communication is only possible if the form and function of linguistic utterances are firmly related to one another. Grammar has its own rules of use to formulate an easier-to-understand language. Like a tool, grammar formulates our thoughts and knowledge in a meaningful way. Every language has its own grammar. With grammar, we can quickly analyze whether there is any action in this text: (Present, past, future). Knowledge of grammar is an important prerequisite for mastering a foreign language. What’s most important is how teachers can make grammar lessons more interesting for students and thus promote grammar skills more successfully. Through this paper, we discuss a few important methods like (Interactive Grammar Exercises between students, Interactive Grammar Exercise between student to teacher, Grammar translation method, Audio -Visual Method, Deductive Method, Inductive Method). This paper is divided into two sections. In the first part, brief definitions and principles of these approaches will be provided. Then the possibility and the case of combination of this approach will be analyzed. In the last section of the paper, I would like to present a survey result conducted at my university on a few methods to quickly learn grammar in Foreign Language. We divided the Grammatical Skills in six Parts. 1.Grammatical Competence 2. Speaking Skills 3. Phonology 4. The syntax and the Semantics 5. Rule 6. Cognitive Function and conducted a survey among students. From our survey results, we can observe that phonology, speaking ability, syntax and semantics can be improved by inductive method, Audio-visual Method, and grammatical translation method, for grammar rules and cognitive functions we should choose IGE (teacher-student) method. and the IGE method (pupil-pupil). The study’s findings revealed, that the teacher delivery Methods should be blend or fusion based on the content of the Grammar.

Keywords: innovative method, grammatical skills, audio-visual, translation

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Authors: Khadra Ahmed, Khaled Kheireldin

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In this paper, we present a pedestrian detection descriptor called Fused Structure and Texture (FST) features based on the combination of the local phase information with the texture features. Since the phase of the signal conveys more structural information than the magnitude, the phase congruency concept is used to capture the structural features. On the other hand, the Center-Symmetric Local Binary Pattern (CSLBP) approach is used to capture the texture information of the image. The dimension less quantity of the phase congruency and the robustness of the CSLBP operator on the flat images, as well as the blur and illumination changes, lead the proposed descriptor to be more robust and less sensitive to the light variations. The proposed descriptor can be formed by extracting the phase congruency and the CSLBP values of each pixel of the image with respect to its neighborhood. The histogram of the oriented phase and the histogram of the CSLBP values for the local regions in the image are computed and concatenated to construct the FST descriptor. Several experiments were conducted on INRIA and the low resolution DaimlerChrysler datasets to evaluate the detection performance of the pedestrian detection system that is based on the FST descriptor. A linear Support Vector Machine (SVM) is used to train the pedestrian classifier. These experiments showed that the proposed FST descriptor has better detection performance over a set of state of the art feature extraction methodologies.

Keywords: planktons, diversity indices, water quality index, water ways

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Authors: Malak Alasli

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Hungarians come to use geographic names that are foreign to their environment and language in diverse settings, hence the aim of trying to adapt them to their own linguistic context. The Maghreb region (Morocco, Algeria, and Tunisia) uses both Arabic and French in presenting the place names. Consequently, the lexicographical treatment of the toponym will, therefore, consist of both the presentation of the toponymic term and the pronunciation of the entries. The motivation behind this approach is the need for a better identification of the place in question by avoiding ambiguities, and for more respect to the heritage by conforming to the right use of toponyms both in written as well as in oral practice. The goal is to provide Hungarians with a set of data by attempting a system of transliteration from French/Arabic to Hungarian, where the place names of the Maghreb are transliterated for more efficient usage. To examine the importance of toponyms’ pronunciation, the latter were collected from several 20th and 21st Hungarian school atlases. Most people meet, for the first time, foreign place names in school, hence the choice of solely extracting place names from school atlases as sample data. Interviews targeted university students, where they were asked to pronounce the place names collected. Results revealed the intricacy behind the pronunciation. Two main conclusions emerged; Hungarian students encountered challenges reading the toponyms, and Arabic speakers could not identify the names either, which causes a cut in communication. Ergo, the importance of elaborating on the pronunciation of toponyms. Concordance is where you find variants of a name. Therefore, a chart was put forward including all the name variants obtained from various references with their Arabic transcription indicating any changes that may have occurred, and the origin of the denomination (Roman, Berber, etc.). A case will also be added for comments and observations. This work embraces a dual purpose. It will provide information to Hungarians on the official names of foreign places in case of occurring changes; for instance, 'El-goléa, Algeria' (used in a latest edition of a school atlas) has now the official name of 'El Ménia'. It will also serve as a reference for knowing the correct and precise forms of place names’ pronunciation.

Keywords: concordance, onomastics, settlement names, school atlases

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Authors: Chia-Min Lai, Wan-Ching Lin, Hahn-Ming Lee, Ching-Hao Mao

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As world wild internet has non-stop developments, making profit by lending registered domain names emerges as a new business in recent years. Unfortunately, the larger the market scale of domain lending service becomes, the riskier that there exist malicious behaviors or malwares hiding behind parked domains will be. Also, previous work for differentiating parked domain suffers two main defects: 1) too much data-collecting effort and CPU latency needed for features engineering and 2) ineffectiveness when detecting parked domains containing external links that are usually abused by hackers, e.g., drive-by download attack. Aiming for alleviating above defects without sacrificing practical usability, this paper proposes ParkedGuard as an efficient and accurate parked domain detector. Several scripting behavioral features were analyzed, while those with special statistical significance are adopted in ParkedGuard to make feature engineering much more cost-efficient. On the other hand, finding memberships between external links and parked domains was modeled as a graph mining problem, and a coarse-to-fine strategy was elaborately designed by leverage the graphical locality such that ParkedGuard outperforms the state-of-the-art in terms of both recall and precision rates.

Keywords: coarse-to-fine strategy, domain parking service, graphical locality analysis, parked domain

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Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

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Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

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Authors: W. Ling, Mohamed Saufi Bin Awang

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Introduction: Mycobacterium tuberculosis post a major health problem worldwide. Central nervous system (CNS) infection by mycobacterium tuberculosis is one of the most devastating complications of tuberculosis. Although with advancement in medical fields, we are yet to understand the pathophysiology of how mycobacterium tuberculosis was able to cross the blood-brain barrier (BBB) and infect the CNS. CNS TB may present with nonspecific clinical symptoms which can mimic other diseases/conditions; this is what makes the diagnosis relatively difficult and challenging. Public health has to be informed and educated about the spread of TB, and early identification of TB is important as it is a curable disease. Case Report: A young 21-year-old Malay gentleman was initially presented to us with symptoms of ear discharge, tinnitus, and right-sided headache for the past one year. Further history reveals that the symptoms have been mismanaged and neglected over the period of 1 year. Initial investigation reveals features of inflammation of the ear. Further imaging showed the feature of chronic inflammation of the otitis media and atypical right cerebral abscess, which has the same characteristic features and consistency. He further underwent a biopsy, and results reveal positive Mycobacterium tuberculosis of the otitis media. With the results and the available imaging, we were certain that this is likely a case of disseminated tuberculosis causing CNS TB. Conclusion: We aim to highlight the challenge and difficult face in our health care system and public health in early identification and treatment.

Keywords: central nervous system tuberculosis, intracranial tuberculosis, tuberculous encephalopathy, tuberculous meningitis

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Authors: Rafat Y. Alwazna

Abstract:

Certain religious texts are deemed part of legal texts that are characterised by high sensitivity and sacredness. Amongst such religious texts are Islamic legal texts that are replete with Islamic legal terms that designate particular legal concepts peculiar to Islamic legal system and legal culture. However, from the syntactic perspective, Islamic legal texts prove lengthy, condensed and convoluted, with little use of punctuation system, but with an extensive use of subordinations and co-ordinations, which separate the main verb from the subject, and which, of course, carry a heavy load of legal detail. The present paper seeks to examine the syntactic features of Islamic legal texts through analysing a short text of Islamic jurisprudence in an attempt at exploring the syntactic features that characterise this type of legal text. A translation of this text into legal English is then exercised to find the translation implications that have emerged as a result of the English translation. Based on these implications, the paper compares and contrasts the syntactic features of Islamic legal texts to those of legal English texts. Finally, the present paper argues that there are a number of syntactic features of Islamic legal texts, such as nominalisation, passivisation, little use of punctuation system, the use of the Arabic cohesive device, etc., which are also possessed by English legal texts except for the last feature and with some variations. The paper also claims that when rendering an Islamic legal text into legal English, certain implications emerge, such as the necessity of a sentence break, the omission of the cohesive device concerned and the increase in the use of nominalisation, passivisation, passive participles, and so on.

Keywords: English legal texts, Islamic legal texts, nominalisation, participles, passivisation, syntactic features, translation implications

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Parisa Mansour

Abstract:

The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

Procedia PDF Downloads 137