Search results for: character recognition

Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: Fatima Muhammad Shitu

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This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied.

Keywords: keyword, lexical, semantics, strategy

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Authors: Thiago Spilborghs Bueno Meyer, Plinio Thomaz Aquino Junior

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Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs.

Keywords: emotion recognition, speech, deep learning, human-robot interaction, neural networks

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Authors: Mei-Chun Chang

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Due to digital transformation and social media development in recent years, various real-time interactive digital tools have been developed to meet the design demands for virtual reality avatars, which also promote digital content learners' active participation in the creation process. As a result, new social media design tools have the characteristics of intuitive operation with a simplified interface for fast production, from which works can be simply created. This study carried out observations, records, questionnaire surveys, and interviews on the creation and learning of visual avatars made by students of the National Taiwan University of Science and Technology (NTUST) with the VRoid Studio 3D modeling tool so as to explore their learning effectiveness on the design of visual avatars. According to the results of this study, the VRoid Studio 3D character modeling tool has a positive impact on the learners and helps to improve their learning effectiveness. Students with low academic achievements said that they could complete the conceived modeling with their own thinking by using the design tool, which increased their sense of accomplishment. Conclusions are drawn according to the results, and relevant future suggestions are put forward.

Keywords: virtual avatar, character design, social media, vroid studio, creation, digital learning

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Authors: Mahshid Arabi

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In recent years, remarkable advancements in the fields of artificial intelligence and machine learning have led to the development of facial recognition technologies. These technologies are now employed in a wide range of applications, including security, surveillance, healthcare, and education. In the field of education, the identification of university entrance exam candidates has been one of the fundamental challenges. Traditional methods such as using ID cards and handwritten signatures are not only inefficient and prone to fraud but also susceptible to errors. In this context, utilizing advanced technologies like facial recognition can be an effective and efficient solution to increase the accuracy and reliability of identity verification in entrance exams. This article examines the use of FaceMatch software for recognizing the faces of university entrance exam candidates in Iran. The main objective of this research is to evaluate the efficiency and accuracy of FaceMatch software in identifying university entrance exam candidates to prevent fraud and ensure the authenticity of individuals' identities. Additionally, this research investigates the advantages and challenges of using this technology in Iran's educational systems. This research was conducted using an experimental method and random sampling. In this study, 1000 university entrance exam candidates in Iran were selected as samples. The facial images of these candidates were processed and analyzed using FaceMatch software. The software's accuracy and efficiency were evaluated using various metrics, including accuracy rate, error rate, and processing time. The research results indicated that FaceMatch software could accurately identify candidates with a precision of 98.5%. The software's error rate was less than 1.5%, demonstrating its high efficiency in facial recognition. Additionally, the average processing time for each candidate's image was less than 2 seconds, indicating the software's high efficiency. Statistical evaluation of the results using precise statistical tests, including analysis of variance (ANOVA) and t-test, showed that the observed differences were significant, and the software's accuracy in identity verification is high. The findings of this research suggest that FaceMatch software can be effectively used as a tool for identifying university entrance exam candidates in Iran. This technology not only enhances security and prevents fraud but also simplifies and streamlines the exam administration process. However, challenges such as preserving candidates' privacy and the costs of implementation must also be considered. The use of facial recognition technology with FaceMatch software in Iran's educational systems can be an effective solution for preventing fraud and ensuring the authenticity of university entrance exam candidates' identities. Given the promising results of this research, it is recommended that this technology be more widely implemented and utilized in the country's educational systems.

Keywords: facial recognition, FaceMatch software, Iran, university entrance exam

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Authors: Tamir Michal

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The subject of Bedouin dispersion has accompanied the State of Israel from the day of its establishment. From a legal point of view, this subject has offered a launchpad for creative judicial decisions. Thus, for example, the first court decision in Israel to recognize affirmative action (Avitan), dealt with a petition submitted by a Jew appealing the refusal of the State to recognize the Petitioner’s entitlement to the long-term lease of a plot designated for Bedouins. The Supreme Court dismissed the petition, holding that there existed a public interest in assisting Bedouin to establish permanent urban settlements, an interest which justifies giving them preference by selling them plots at subsidized prices. In another case (The Forum for Coexistence in the Negev) the Supreme Court extended equitable relief for the purpose of constructing a bridge, even though the construction infringed the Law, in order to allow the children of dispersed Bedouin to reach school. Against this background, the recent verdict, delivered during the Protective Edge military campaign, which dismissed a petition aimed at forcing the State to spread out Protective Structures in Bedouin villages in the Negev against the risk of being hit from missiles launched from Gaza (Abu Afash) is disappointing. Even if, in arguendo, no selective discrimination was involved in the State’s decision not to provide such protection, the decision, and its affirmation by the Court, is problematic when examined through the prism of the Theory of Recognition. The article analyses the issue by tools of theory of Recognition, according to which people develop their identities through mutual relations of recognition in different fields. In the social context, the path to recognition is cognitive respect, which is provided by means of legal rights. By seeing other participants in Society as bearers of rights and obligations, the individual develops an understanding of his legal condition as reflected in the attitude to others. Consequently, even if the Court’s decision may be justified on strict legal grounds, the fact that Jewish settlements were protected during the military operation, whereas Bedouin villages were not, is a setback in the struggle to make the Bedouin citizens with equal rights in Israeli society. As the Court held, ‘Beyond their protective function, the Migunit [Protective Structures] may make a moral and psychological contribution that should not be undervalued’. This contribution is one that the Bedouin did not receive in the Abu Afash verdict. The basic thesis is that the Court’s verdict analyzed above clearly demonstrates that the reliance on classical liberal instruments (e.g., equality) cannot secure full appreciation of all aspects of Bedouin life, and hence it can in fact prejudice them. Therefore, elements of the recognition theory should be added, in order to find the channel for cognitive dignity, thereby advancing the Bedouins’ ability to perceive themselves as equal human beings in the Israeli society.

Keywords: bedouin dispersion, cognitive respect, recognition theory, sustainable development

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Authors: Agustinus Rudatyo Himamunanto, Anastasia Rita Widiarti

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Transliteration using Javanese manuscripts is one of methods to preserve and legate the wealth of literature in the past for the present generation in Indonesia. The transliteration manual process commonly requires philologists and takes a relatively long time. The automatic transliteration process is expected to shorten the time so as to help the works of philologists. The preprocessing and segmentation stage firstly done is used to manage the document images, thus obtaining image script units that will compile input document images free from noise and have the similarity in properties in the thickness, size, and slope. The next stage of characteristic extraction is used to find unique characteristics that will distinguish each Javanese script image. One of characteristics that is used in this research is the number of black pixels in each image units. Each image of Java scripts contained in the data training will undergo the same process similar to the input characters. The system testing was performed with the data of the book Hamong Tani. The book Hamong Tani was selected due to its content, age and number of pages. Those were considered sufficient as a model experimental input. Based on the results of random page automatic transliteration process testing, it was determined that the maximum percentage correctness obtained was 81.53%. The percentage of success was obtained in 32x32 pixel input image size with the 5x5 image window. With regard to the results, it can be concluded that the automatic transliteration model offered is relatively good.

Keywords: Javanese script, character recognition, statistical, automatic transliteration

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Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

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The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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Authors: T. Thein, S. Kalyar Myo

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Human uses visual information for understanding the speech contents in noisy conditions or in situations where the audio signal is not available. The primary advantage of visual information is that it is not affected by the acoustic noise and cross talk among speakers. Using visual information from the lip movements can improve the accuracy and robustness of automatic speech recognition. However, a major challenge with most automatic lip reading system is to find a robust and efficient method for extracting the linguistically relevant speech information from a lip image sequence. This is a difficult task due to variation caused by different speakers, illumination, camera setting and the inherent low luminance and chrominance contrast between lip and non-lip region. Several researchers have been developing methods to overcome these problems; the one is lip reading. Moreover, it is well known that visual information about speech through lip reading is very useful for human speech recognition system. Lip reading is the technique of a comprehensive understanding of underlying speech by processing on the movement of lips. Therefore, lip reading system is one of the different supportive technologies for hearing impaired or elderly people, and it is an active research area. The need for lip reading system is ever increasing for every language. This research aims to develop a visual teaching method system for the hearing impaired persons in Myanmar, how to pronounce words precisely by identifying the features of lip movement. The proposed research will work a lip reading system for Myanmar Consonants, one syllable consonants (င (Nga)၊ ည (Nya)၊ မ (Ma)၊ လ (La)၊ ၀ (Wa)၊ သ (Tha)၊ ဟ (Ha)၊ အ (Ah) ) and two syllable consonants ( က(Ka Gyi)၊ ခ (Kha Gway)၊ ဂ (Ga Nge)၊ ဃ (Ga Gyi)၊ စ (Sa Lone)၊ ဆ (Sa Lain)၊ ဇ (Za Gwe) ၊ ဒ (Da Dway)၊ ဏ (Na Gyi)၊ န (Na Nge)၊ ပ (Pa Saug)၊ ဘ (Ba Gone)၊ ရ (Ya Gaug)၊ ဠ (La Gyi) ). In the proposed system, there are three subsystems, the first one is the lip localization system, which localizes the lips in the digital inputs. The next one is the feature extraction system, which extracts features of lip movement suitable for visual speech recognition. And the final one is the classification system. In the proposed research, Two Dimensional Discrete Cosine Transform (2D-DCT) and Linear Discriminant Analysis (LDA) with Active Contour Model (ACM) will be used for lip movement features extraction. Support Vector Machine (SVM) classifier is used for finding class parameter and class number in training set and testing set. Then, experiments will be carried out for the recognition accuracy of Myanmar consonants using the only visual information on lip movements which are useful for visual speech of Myanmar languages. The result will show the effectiveness of the lip movement recognition for Myanmar Consonants. This system will help the hearing impaired persons to use as the language learning application. This system can also be useful for normal hearing persons in noisy environments or conditions where they can find out what was said by other people without hearing voice.

Keywords: feature extraction, lip reading, lip localization, Active Contour Model (ACM), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Two Dimensional Discrete Cosine Transform (2D-DCT)

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Authors: Barrere Sarah

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The area of liberty, security, and justice within the European Union is still a work in progress. No one can deny that the EU struggles between a monistic and a dualist approach. The aim of our essay is to first review how the European law is perceived by the rest of the international scene. It will then discuss two main mechanisms at play: the interpretation of larger international treaties and the penal mechanisms of European law. Finally, it will help us understand the role of a penal Europe on the international scene with concrete examples. Special attention will be paid to cases that deal with fundamental rights as they represent an interesting case study in Europe and in the rest of the World. It could illustrate the aforementioned duality currently present in the Union’s interpretation of international public law. On the other hand, it will explore some specific European penal mechanism through mutual recognition and the European arrest warrant in the transnational criminality frame. Concerning the interpretation of the treaties, it will first, underline the ambiguity and the general nature of some treaties that leave the EU exposed to tension and misunderstanding then it will review the validity of an EU act (whether or not it is compatible with the rules of International law). Finally, it will focus on the most complete manifestation of liberty, security and justice through the principle of mutual recognition. Used initially in commercial matters, it has become “the cornerstone” of European construction. It will see how it is applied in judicial decisions (its main event and achieving success is via the European arrest warrant) and how European member states have managed to develop this cooperation.

Keywords: European penal law, international scene, liberty security and justice area, mutual recognition

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Authors: Ebim Matthew Abua

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In Chinua Achebe’s Things Fall Apart (henceforth, TFA), the character Unoka is portrayed as a weakling and indolent person even when he was a creative artist, a talented musician, and a mathematician. His lack of achievement becomes the barometer for measuring his success. Right from time, music is considered to be of great significance because of its capacity to recreate and retell social events. To this end, music is both a social act and a creative art. As a social act, music is a discursive medium that exploits the dynamics of art in its evaluation of society. Music is so much a part of human existence that its presence in literature can help readers relate to fictional situations and characters. In this paper, the character Unoka is examined against the backdrop of his musical proclivities and his contributions to the overall development of TFA. Unfortunately, Achebe’s Things Fall Apart, a product of artistic creativity, portrays the personality of Unoka as lazy and uninspiring because he (Unoka) is a musician who is busy playing his flute and hardly doing anything productive. This paper is significant because it examines the literary and or linguistic depiction of Unoka and the aftermath of that depiction on the entire novel and, by extension, the larger society. Methodologically, this paper adopted the qualitative approach from the ethnography of communication (EOC), which is the analysis of communication within the wider context of the social and cultural practices and beliefs of the members of a particular culture or speech community. The aim of this qualitative research method includes the ability to discern which communication acts and/or codes are important to different groups, what types of meanings groups apply to different communication events, and how group members learn these codes to provide insight into particular communities. The study reveals that the people of Umuofia were mono-directional in their economy, and there was no room for diversification. One was either a farmer, or such a person was relegated to the background. Unoka, taking up a new challenge of diversifying the economy from the perspective of entertainment, was viewed as a misnomer. This study opens the door to other areas of studying Achebe’s epic novel apart from the critical works of literary artists that have been dished out over time.

Keywords: literature, popular culture, unoka, things fall apart

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Authors: Charles W. MacQuarrie, Rachel R. Tatro Duarte

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This paper explores the representation of medieval identity within the world of games such as Dragon Age, Elden Ring, Hellblade: Senua’s sacrifice, fantasy role-playing games that draw effectively and problematically on Celtic and Norse mythologies. Focusing on tattoos, onomastics, and accent as visual and oral markers of status and ethnicity, this study analyzes how the game's interplay between mythology, character narratives, and visual storytelling enriches the themes and offers players an immersive, but sometimes baldly ahistorical, connection to ancient mythologies and contemporary digital storytelling. Dragon Age is a triple a game series, Hellblade Senua’s Sacrifice, and Elden Ring of gamers worldwide with its presentation of an idealized medieval world, inspired by the lore of Celtic and Norse mythologies. This paper sets out to explore the intricate relationships between tattoos, accent, and character narratives in the game, drawing parallels to themes,heroic figures and gods from Celtic and Norse mythologies. Tattoos as Mythic and Ethnic Markers: This study analyzes how tattoos in Dragon Age visually represent mythological elements from both Celtic and Norse cultures, serving as conduits of cultural identity and narratives. The nature of these tattoos reflects the slave, criminal, warrior associations made in classical and medieval literature, and some of the episodes concerning tattoos in the games have either close analogs or sources in literature. For example the elvish character Solas, in Dragon Age Inquisition, removes a slave tattoo from the face of a lower status elf in an episode that is reminiscent of Bridget removing the stigmata from Connallus in the Vita Prima of Saint Bridget Character Narratives: The paper examines how characters' personal narratives in the game parallel the archetypal journeys of Celtic heroes and Norse gods, with a focus on their relationships to mythic themes. In these games the Elves usually have Welsh or Irish accents, are close to nature, magically powerful, oppressed by apparently Anglo-Saxon humans and Norse dwarves, and these elves wear facial tattoos. The Welsh voices of fairies and demons is older than the reference in Shakespeare’s Merry Wives of Windsor or even the Anglo-Saxon Life of Saint Guthlac. The English speaking world, and the fantasy genre of literature and gaming, undoubtedly driven by Tolkien, see Elves as Welsh speakers, and as having Welsh accents when speaking English Comparative Analysis: A comparative approach is employed to reveal connections, adaptations, and unique interpretations of the motifs of tattoos and narrative themes in Dragon Age, compared to those found in Celtic and Norse mythologies. Methodology: The study uses a comparative approach to examine the similarities and distinctions between Celtic and Norse mythologies and their counterparts in video games. The analysis encompasses character studies, narrative exploration, visual symbolism, and the historical context of Celtic and Norse cultures. Mythic Visuals: This study showcases how tattoos, as visual symbols, encapsulate mythic narratives, beliefs, and cultural identity, echoing Celtic and Norse visual motifs. Archetypal Journeys: The paper analyzes how character arcs mirror the heroic journeys of Celtic and Norse mythological figures, allowing players to engage with mythic narratives on a personal level. Cultural Interplay: The study discusses how the game's portrayal of tattoos and narratives both preserves and reinterprets elements from Celtic and Norse mythologies, fostering a connection between ancient cultures and modern digital storytelling. Conclusion: By exploring the interconnectedness of tattoos and character narratives in Dragon Age, this paper reveals the game series' ability to act as a bridge between ancient mythologies and contemporary gaming. By drawing inspiration from Celtic heroes and Norse gods and translating them into digital narratives and visual motifs, Dragon Age offers players a multi-dimensional engagement with mythic themes and a unique lens through which to appreciate the enduring allure of these cultures.

Keywords: comparative analysis, character narratives, video games and literature, tattoos, immersive storytelling, character development, mythological influences, Celtic mythology, Norset mythology

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Authors: Maryam Vaezi

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In this study, the Functional Theory of Political Campaign Discourse has been applied in order to investigate the 2016 Clinton-Trump presidential debates. All three kinds of utterances (acclaims, attacks, and defenses) were produced by the candidates supporting the usefulness of the Functional Theory of Political Campaign Discourse for the analysis of the presidential debates as a type of political discourse. Attacks comprised 45% of the candidates’ utterances, followed by acclaims at 33%; defenses were the least common function at 22%. The candidate from the Democratic Party, Hillary Clinton, acclaimed more, whereas the Republican Party presidential candidate, Donald Trump, attacked more. Simple denial was the most common form of defense used by the candidates. Both candidates directed more of their utterances to policy (past deeds, future plans, and general goals) than character (personal qualities, leadership abilities, and ideals). Analyzing debates in terms of the functions performed by the candidates to increase their desirability and chance of winning the election, can lead to a better understanding of these significant political events as well as other forms of political discourse.

Keywords: acclaim, attack, defend, character, Democratic Party, Donald Trump, Hillary Clinton, policy, presidential debates, Republican Party

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Authors: S. Ali, R. Yunus, A. Arif, Y. Ayaz, M. Baber Sial, R. Asif, N. Naseer, M. Jawad Khan

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In this study, we have proposed a gesture to emotion recognition method using flex sensors mounted on metacarpophalangeal joints. The flex sensors are fixed in a wearable glove. The data from the glove are sent to PC using Wi-Fi. Four gestures: finger pointing, thumbs up, fist open and fist close are performed by five subjects. Each gesture is categorized into sad, happy, and excited class based on the velocity and acceleration of the hand gesture. Seventeen inspectors observed the emotions and hand gestures of the five subjects. The emotional state based on the investigators assessment and acquired movement speed data is compared. Overall, we achieved 77% accurate results. Therefore, the proposed design can be used for emotional state detection applications.

Keywords: emotion identification, emotion models, gesture recognition, user perception

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Authors: Liron Hoch

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Traditional religious and spiritual texts offer a surprising wealth of relevant theoretical and practical knowledge about human behavior. This wellspring may contribute significantly to expanding our current body of knowledge in the social sciences and criminology in particular. In Jewish religious texts, specifically by Maimonides, we can find profound analyses of human traits and guidelines for a normative way of life. Among other things, modern criminological literature attempts to link certain character traits and divergent behaviors. Using the hermeneutic phenomenological approach, we analyzed the writings of Maimonides, mainly Laws of Human Dispositions, in order to understand Moses ben Maimon's (1138–1204) view of character traits. The analysis yielded four themes: (1) Human personality between nature and nurture; (2) The complexity of human personality, imbalance and criminality; (3) Extremism as a way to achieve balance; and (4) The Middle Way, flexibility and common sense. These themes can serve therapeutic purposes, as well as inform a rehabilitation model. Grounded in a theoretical rationale about the nature of humans, this model is designed to direct individuals to balance their traits by self-reflection and constant practice of the Middle Way. The proposal we will present is that implementing this model may promote normative behavior and thus contribute to rehabilitating offenders.

Keywords: rehabilitation, traits, offenders, maimonides, middle way

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Authors: Sumaira Mukhtar

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The present research aims at the discussion of the subversion of the hegemony of the feminine self in the text Moth Smoke by a Pakistani novelist Mohsin Hamid. It presents the notion of the subversion of the grand narratives of the ‘positioning’ of feminine identity in Pakistani patriarchal society by presenting a de-stereotyped personality of Mumtaz, the protagonist in Moth Smoke. The dominant masculine traits in Mumtaz’s personality have been negotiated since she is an untraditional female character in the novel. In this regard, the researcher has taken a feministic stance in this study by presenting the proposition that subaltern can also speak. Mumtaz’s character reminds one of Hedda from Henrik Ibsen’s play Hedda Gabler. So, the masculine traits in Mumtaz’s personality have also been compared with Hedda’s. Besides, the research study will also bring into notice that how that in the postmodern scenario, marginalization of the women have been responded back by women and hereby Mumtaz by uplifting her social status and class. Her de-stereotyped feminine self has been reinforced by the dialogues and incidents in the text. This research is qualitative in design and is based on the textual analysis. An interpretive research method has also been utilized since the researcher has tried to decode the text in supporting the notion of de-stereotyping of feminine self. This research would add to the body of Pakistani literature and Feministic theory.

Keywords: de-stereotyped, feminine identity, marginalization, masculine traits

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Lumindu Dilumka, Ranaweera I. D., Sudusinghe S. P., Sanduni Kanchana A. M. K.

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This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore, parents, guardians and teachers must ensure the safety of children in cyberspace. Children are more likely to go astray and there are plenty of online programs are waiting to get them on the wrong track and also, children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that, due to the unawareness of children, they tempt to share their sensitive information, causing a chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system.

Keywords: online education, cyber bullying, social media, face recognition, web tracker, privacy data

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Ines Elouedi, Atef Hammouda

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This paper presents a new fast version of the generalized Multi-Directional Radon Transform method. The new method uses the inverse Fast Fourier Transform to lead to a faster Generalized Radon projections. We prove in this paper that the fast algorithm leads to almost the same results of the eldest one but with a considerable lower time computation cost. The projection end result of the fast method is a parameterized Radon space where a high valued pixel allows the detection of a curve from the original image. The proposed fast inversion algorithm leads to an exact reconstruction of the initial image from the Radon space. We show examples of the impact of this algorithm on the pattern recognition domain.

Keywords: fast generalized multi-directional Radon transform, curve, exact reconstruction, pattern recognition

Procedia PDF Downloads 277

Authors: C. Hamamura, V. Gialluca

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Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan.

Keywords: image pattern recognition, trees pruning, trees recognition, neural network

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Authors: Hajer Rahali, Zied Hajaiej, Noureddine Ellouze

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The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method. In our work the effectiveness of proposed changes to MFCC called Modified Function Cepstral Coefficients (MODFCC) were tested and compared against the original MFCC and RASTA-MFCC features. The prosodic features such as jitter and shimmer are added to baseline spectral features. The above-mentioned techniques were tested with impulsive signals under various noisy conditions within AURORA databases.

Keywords: auditory filter, impulsive noise, MFCC, prosodic features, RASTA filter

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Thushel Jayaweera

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In performance evaluation literature, there has been no investigation indicating the impact of job characteristics, working conditions and motivation on the job performance among the hotel workers in Britain. This study tested the relationship between working conditions (physical and psychosocial working conditions) and job performance (task and contextual performance) with motivators (e.g. recognition, achievement, the work itself, the possibility for growth and work significance) as the mediating variable. A total of 254 hotel workers in 25 hotels in Bristol, United Kingdom participated in this study. Working conditions influenced job performance and motivation moderated the relationship between working conditions and job performance. Poor workplace conditions resulted in decreasing employee performance. The results point to the importance of motivators among hotel workers and highlighted that work be designed to provide recognition and sense of autonomy on the job to enhance job performance of the hotel workers. These findings have implications for organizational interventions aimed at increasing employee job performance.

Keywords: hotel workers, working conditions, motivation, job characteristics, job performance

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Authors: Jianan Sun, Ziwen Ye

Abstract:

Multidimensional computerized adaptive testing (MCAT) is a popular research topic in psychometrics. It is important for practitioners to clearly know the item-trait patterns of administered items when a test like MCAT is operated. Item-trait pattern recognition refers to detecting which latent traits in a psychological test are measured by each of the specified items. If the item-trait patterns of the replenished items in MCAT item pool are well detected, the interpretability of the items can be improved, which can further promote the abilities of the examinees who attending the MCAT to be accurately estimated. This research explores to solve the item-trait pattern recognition problem of the replenished items in MCAT item pool from the perspective of statistical variable selection. The popular multidimensional item response theory model, multidimensional two-parameter logistic model, is assumed to fit the response data of MCAT. The proposed method uses the least absolute shrinkage and selection operator (LASSO) to detect item-trait patterns of replenished items based on the essential information of item responses and ability estimates of examinees collected from a designed MCAT procedure. Several advantages of the proposed method are outlined. First, the proposed method does not strictly depend on the relative order between the replenished items and the selected operational items, so it allows the replenished items to be mixed into the operational items in reasonable order such as considering content constraints or other test requirements. Second, the LASSO used in this research improves the interpretability of the multidimensional replenished items in MCAT. Third, the proposed method can exert the advantage of shrinkage method idea for variable selection, so it can help to check item quality and key dimension features of replenished items and saves more costs of time and labors in response data collection than traditional factor analysis method. Moreover, the proposed method makes sure the dimensions of replenished items are recognized to be consistent with the dimensions of operational items in MCAT item pool. Simulation studies are conducted to investigate the performance of the proposed method under different conditions for varying dimensionality of item pool, latent trait correlation, item discrimination, test lengths and item selection criteria in MCAT. Results show that the proposed method can accurately detect the item-trait patterns of the replenished items in the two-dimensional and the three-dimensional item pool. Selecting enough operational items from the item pool consisting of high discriminating items by Bayesian A-optimality in MCAT can improve the recognition accuracy of item-trait patterns of replenished items for the proposed method. The pattern recognition accuracy for the conditions with correlated traits is better than those with independent traits especially for the item pool consisting of comparatively low discriminating items. To sum up, the proposed data-driven method based on the LASSO can accurately and efficiently detect the item-trait patterns of replenished items in MCAT.

Keywords: item-trait pattern recognition, least absolute shrinkage and selection operator, multidimensional computerized adaptive testing, variable selection

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Authors: Murat Durucu

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Glaucoma is a condition of irreversible blindness, early diagnosis and appropriate interventions to make the patients able to see longer time. In this study, it addressed that the importance of developing a decision support system for glaucoma diagnosis. Glaucoma occurs when pressure happens around the eyes it causes some damage to the optic nerves and deterioration of vision. There are different levels ranging blindness of glaucoma disease. The diagnosis at an early stage allows a chance for therapies that slows the progression of the disease. In recent years, imaging technology from Heidelberg Retinal Tomography (HRT), Stereoscopic Disc Photo (SDP) and Optical Coherence Tomography (OCT) have been used for the diagnosis of glaucoma. This better accuracy and faster imaging techniques in response technique of OCT have become the most common method used by experts. Although OCT images or HRT precision and quickness, especially in the early stages, there are still difficulties and mistakes are occurred in diagnosis of glaucoma. It is difficult to obtain objective results on diagnosis and placement process of the doctor's. It seems very important to develop an objective decision support system for diagnosis and level the glaucoma disease for patients. By using OCT images and pattern recognition systems, it is possible to develop a support system for doctors to make their decisions on glaucoma. Thus, in this recent study, we develop an evaluation and support system to the usage of doctors. Pattern recognition system based computer software would help the doctors to make an objective evaluation for their patients. It is intended that after development and evaluation processes of the software, the system is planning to be serve for the usage of doctors in different hospitals.

Keywords: decision support system, glaucoma, image processing, pattern recognition

Procedia PDF Downloads 302

Authors: Bekkem Chakradhar Reddy, Y. Soni Priya, Mathivanan G., L. K. Joshila Grace, N. Srinivasan, Asha P.

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Currently, there are a lot of problems related to marking attendance in schools, offices, or other places. Organizations tasked with collecting daily attendance data have numerous concerns. There are different ways to mark attendance. The most commonly used method is collecting data manually by calling each student. It is a longer process and problematic. Now, there are a lot of new technologies that help to mark attendance automatically. It reduces work and records the data. We have proposed to implement attendance marking using the latest technologies. We have implemented a system based on face identification and analyzing faces. The project is developed by gathering faces and analyzing data, using deep learning algorithms to recognize faces effectively. The data is recorded and forwarded to the host through mail. The project was implemented in Python and Python libraries used are CV2, Face Recognition, and Smtplib.

Keywords: python, deep learning, face recognition, CV2, smtplib, Dlib.

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Authors: Antonio D. Lee, Steven X. Jiang

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A variety of techniques and methods are available to evaluate cognitive performance in Urban Search and Rescue (USAR) applications. However, traditional cognitive performance evaluation techniques typically incorporate either the conscious or systematic aspect, failing to take into consideration the subconscious or intuitive aspect. This leads to incomplete measures and produces ineffective designs. In order to fill the gaps in past research, this study developed a theoretical framework to facilitate the integration of situation awareness (SA) and intuitive pattern recognition (IPR) to enhance the cognitive performance representation in USAR applications. This framework provides guidance to integrate both SA and IPR in order to evaluate the cognitive performance of the USAR responders. The application of this framework will help improve the system design.

Keywords: cognitive performance, intuitive pattern recognition, situation awareness, urban search and rescue

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Authors: Omneya Messallam, Sara S. Fouad

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The Modernist Movement initially flourished in France, Holland, Germany and the Soviet Union. Many architects and designers were inspired and followed its principles. Two of its most important architects (Gerrit Rietveld and Ludwig Mies van de Rohe) were introduced in this paper. Each did not follow the other’s principles and had their own particular rules; however, they shared the same features of the Modernist International Style, such as Anti-historicism, Abstraction, Technology, Function and Internationalism/ Universality. Key Modernist principles translated into high expectations, which sometimes did not meet the inhabitants’ aspirations of living comfortably; consequently, leading to a conflict and misunderstanding between the designer and their clients’ needs. Therefore, historical case studies (the Schroder and the Farnsworth houses) involving two Modernist pioneer architects have been chosen. This paper is an attempt to explore some of the influential factors affecting buildings design such as: needs, gender, and question concerning commonalities between both designers and their clients. The three aspects and two designers explored here have been chosen because they have been influenced the researchers to understand the impact of those factors on the design process, building’s performance, and the dweller’s satisfaction. This is a descriptive/ analytical research based on two historical comparative case studies that involve several steps such as: key evaluation questions (KEQs), observations, document analysis, etc. The methodology is based on data collation and finding validations. The research aims to state a manifest to regulate the relation between architects and their clients to reach the optimum building performance and functional interior design that suits their clients’ needs, reflects the architects’ character, and the school they belong to. At the end, through the investigation in this paper, the different needs between both the designers and the clients have been seen not only in the building itself but also it could convert the inhabitant’s life in various ways. Moreover, a successful relationship between the architect and their clients could play a significant role in the success of projects. In contrast, not every good design or celebrated building could end up with a successful relationship between the designer and their client or full-fill the inhabitant’s aspirations.

Keywords: architect’s character, building’s performance, commonalities, client’s character, gender, modernist movement, needs

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Authors: Ksheeraj Sai Vepuri, Nada Attar

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We as humans use words with accompanying visual and facial cues to communicate effectively. Classifying facial emotion using computer vision methodologies has been an active research area in the computer vision field. In this paper, we propose a simple method for facial expression recognition that enhances accuracy. We tested our method on the FER-2013 dataset that contains static images. Instead of using Histogram equalization to preprocess the dataset, we used Unsharp Mask to emphasize texture and details and sharpened the edges. We also used ImageDataGenerator from Keras library for data augmentation. Then we used Convolutional Neural Networks (CNN) model to classify the images into 7 different facial expressions, yielding an accuracy of 69.46% on the test set. Our results show that using image preprocessing such as the sharpening technique for a CNN model can improve the performance, even when the CNN model is relatively simple.

Keywords: facial expression recognittion, image preprocessing, deep learning, CNN

Procedia PDF Downloads 143