Search results for: voice recognition

Authors: Caio Marcelo Nunes, Anderson da Silva Soares, Gustavo Teodoro Laureano, Clarimar Jose Coelho

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This paper presents a methodology to pixel recognition in aerial images using fuzzy $c$-means algorithm. This algorithm is a alternative to recognize areas considering uncertainties and inaccuracies. Traditional clustering technics are used in recognizing of multispectral images of earth's surface. This technics recognize well-defined borders that can be easily discretized. However, in the real world there are many areas with uncertainties and inaccuracies which can be mapped by clustering algorithms that use fuzzy sets. The methodology presents in this work is applied to multispectral images obtained from Landsat-5/TM satellite. The pixels are joined using the $c$-means algorithm. After, a classification process identify the types of surface according the patterns obtained from spectral response of image surface. The classes considered are, exposed soil, moist soil, vegetation, turbid water and clean water. The results obtained shows that the fuzzy clustering identify the real type of the earth's surface.

Keywords: aerial images, fuzzy clustering, image processing, pattern recognition

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Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

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Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

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Authors: M. Kharrat, G. Moreau, Z. Aboura

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The latest advances in the weaving industry, combined with increasingly sophisticated means of materials processing, have made it possible to produce complex 3D composite structures. Mainly used in aeronautics, composite materials with 3D architecture offer better mechanical properties than 2D reinforced composites. Nevertheless, these materials require a good understanding of their behavior. Because of the complexity of such materials, the damage mechanisms are multiple, and the scenario of their appearance and evolution depends on the nature of the exerted solicitations. The AE technique is a well-established tool for discriminating between the damage mechanisms. Suitable sensors are used during the mechanical test to monitor the structural health of the material. Relevant AE-features are then extracted from the recorded signals, followed by a data analysis using pattern recognition techniques. In order to better understand the damage scenarios of interlock composite materials, a multi-instrumentation was set-up in this work for tracking damage initiation and development, especially in the vicinity of the first significant damage, called macro-damage. The deployed instrumentation includes video-microscopy, Digital Image Correlation, Acoustic Emission (AE) and micro-tomography. In this study, a multi-variable AE data analysis approach was developed for the discrimination between the different signal classes representing the different emission sources during testing. An unsupervised classification technique was adopted to perform AE data clustering without a priori knowledge. The multi-instrumentation and the clustered data served to label the different signal families and to build a learning database. This latter is useful to construct a supervised classifier that can be used for automatic recognition of the AE signals. Several materials with different ingredients were tested under various solicitations in order to feed and enrich the learning database. The methodology presented in this work was useful to refine the damage threshold for the new generation materials. The damage mechanisms around this threshold were highlighted. The obtained signal classes were assigned to the different mechanisms. The isolation of a 'noise' class makes it possible to discriminate between the signals emitted by damages without resorting to spatial filtering or increasing the AE detection threshold. The approach was validated on different material configurations. For the same material and the same type of solicitation, the identified classes are reproducible and little disturbed. The supervised classifier constructed based on the learning database was able to predict the labels of the classified signals.

Keywords: acoustic emission, classifier, damage mechanisms, first damage threshold, interlock composite materials, pattern recognition

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Authors: Husnain Mushtaq, Mary Anjum, Muhammad Aleem

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The main objective of this paper is used to develop a high security in Automatic Teller Machine (ATM). In these system bankers will collect the mobile numbers from the customers and then provide a code on their mobile number. In most country existing ATM machine use the magnetic card reader. The customer is identifying by inserting an ATM card with magnetic card that hold unique information such as card number and some security limitations. By entering a personal identification number, first the customer is authenticated then will access bank account in order to make cash withdraw or other services provided by the bank. Cases of card fraud are another problem once the user’s bank card is missing and the password is stolen, or simply steal a customer’s card & PIN the criminal will draw all cash in very short time, which will being great financial losses in customer, this type of fraud has increase worldwide. So to resolve this problem we are going to provide the solution using “Mobile SMS code” and ATM “PIN code” in order to improve the verify the security of customers using ATM system and confidence in the banking area.

Keywords: PIN, inquiry, biometric, magnetic strip, iris recognition, face recognition

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Authors: Tillie Curran

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This co-production project aimed for an expansive exploration of disabled young people’s hopes and dreams in the context of unprecedented societal changes. The research questions developed with disabled young people acting as peer researchers, ask ‘what does a good life look like now, and, what are your hopes and dreams for the future?’ Disabled children’s childhood studies and an asset-based approach placed the voice of disabled young people at the centre of the research process and inviting participants to ‘think big’! Over 18 months, academics, members of a Centre for Independent Living and peer researchers, came together to facilitate knowledge cafes with fifty disabled young people aged between 14 and 25 in a college and youth club setting. Methods used included trigger questions, photos voice, video, and cartooning. The peer researchers also investigated how house robots and connected autonomous vehicles might support their future aspirations and sense of freedom in this new era with a trip to the university robotic laboratory. Key themes arising from participants’ hopes and dream were about ‘being responsible’, ‘loving’, ‘freedom and happiness’ and a ‘strong sense of self and togetherness’ and suggest alternative narratives and rich visions of the future possibilities for disabled young people. The five key messages peer researchers produced for the report emphasised freedom to define their futures, desires to make the world a better place, to belong and have the chance of their own family life. Thematic analysis, production of the report and impact activities were all co-produced and as the project progressed peer researchers increasingly demonstrated a role as ‘change makers’ and have formed a young people’s co-production group going on into the future. Discussion of the project highlights the factors that made these processes successful and the ethical dilemmas encountered in the context of normalcy. Finally, we consider the implications for all involved as we rethink ‘the future’, not in terms of normative ideals or trajectories, or seeing service ‘transition’ as an end, but in terms of disabled young people’s contribution, participation, freedoms, and possibilities.

Keywords: co-production, disability, robotic, youth

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Authors: Moohyun Lee, Soojung Hur, Yongwan Park

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In this paper is proposed a method uses only LIDAR system to classification an obstacle and determine its type by establishing database for classifying obstacles based on LIDAR. The existing LIDAR system, in determining the recognition of obstruction in an autonomous vehicle, has an advantage in terms of accuracy and shorter recognition time. However, it was difficult to determine the type of obstacle and therefore accurate path planning based on the type of obstacle was not possible. In order to overcome this problem, a method of classifying obstacle type based on existing LIDAR and using the width of obstacle materials was proposed. However, width measurement was not sufficient to improve accuracy. In this research, the width data was used to do the first classification; database for LIDAR intensity data by four major obstacle materials on the road were created; comparison is made to the LIDAR intensity data of actual obstacle materials; and determine the obstacle type by finding the one with highest similarity values. An experiment using an actual autonomous vehicle under real environment shows that data declined in quality in comparison to 3D LIDAR and it was possible to classify obstacle materials using 2D LIDAR.

Keywords: obstacle, classification, database, LIDAR, segmentation, intensity

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Authors: Rubén Valcarce-Álvarez, Francisco Jáñez-Martino, Rocío Alaiz-Rodríguez

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Cybersecurity challenges, including scams, drug sales, the distribution of child sexual abuse material, fake news, and hate speech on both the surface and deep web, have significantly increased over the past decade. Users who post such content often employ strategies to evade detection by automated filters. Among these tactics, text obfuscation plays an essential role in deceiving detection systems. This approach involves modifying words to make them more difficult for automated systems to interpret while remaining sufficiently readable for human users. In this work, we aim at spotting obfuscated words and the employed techniques, such as leetspeak, word inversion, punctuation changes, and mixed techniques. We benchmark Named Entity Recognition (NER) using models from the BERT family as well as two large language models (LLMs), Llama and Mistral, on XX_NER_WordCamouflage dataset. Our experiments evaluate these models by comparing their precision, recall, F1 scores, and accuracy, both overall and for each individual class.

Keywords: natural language processing (NLP), text obfuscation, named entity recognition (NER), deep learning

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Authors: Shareefa Abdullah Al-Maqtari, Ruzaika Omar Basaree, Rafeah Legino

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A pictorial representation of hybrid forms in science-art collaboration has become a crucial issue in the course of exploring a new painting technique development. This is straight related to the reception of an invisible-recognition phenomenology. In hybrid pictorial representation of invisible-recognition phenomenology, the challenging issue is how to depict the pictorial features of indescribable objects from its mental source, modality and transparency. This paper proposes the hybrid technique of painting Demonstrate, Resemble, and Synthesize (DRS) through a combination of the hybrid aspect-recognition representation of understanding picture, demonstrative mod, the number theory, pattern in the modular arithmetic system, and the coherence theory of visual attention in the dynamic scenes representation. Multi-methods digital gaze data analyses, pattern-modular table operation design, and rotation parameter were used for the visualization. In the scientific processes, Eye-trackingvideo-sections based was conducted using Tobii T60 remote eye tracking hardware and TobiiStudioTM analysis software to collect and analyze the eye movements of ten participants when watching the video clip, Alexander Paulikevitch’s performance’s ‘Tajwal’. Results: we found that correlation of fixation count in section one was positively and moderately correlated with section two Person’s (r=.10, p < .05, 2-tailed) as well as in fixation duration Person’s (r=.10, p < .05, 2-tailed). However, a paired-samples t-test indicates that scores were significantly higher for the section one (M = 2.2, SD = .6) than for the section two (M = 1.93, SD = .6) t(9) = 2.44, p < .05, d = 0.87. In the visual process, the exported data of gaze number N was resembled the hybrid forms of visuospatial attention using the table-mod-analyses operation. The explored hybrid guideline was simply applicable, and it could be as alternative approach to the sustainability of contemporary visual arts.

Keywords: science-art collaboration, hybrid forms, pictorial representation, visuospatial attention, modular arithmetic

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Authors: Taha Enes Kon, Vanshika Reddy

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This project focuses on developing a gamified mobile application to aid in stroke rehabilitation by enhancing motor skills through interactive activities. The primary goal was to design a companion app for a passive haptic rehab glove, incorporating Google MediaPipe for gesture tracking and vibrotactile feedback. The app simulates farming activities, offering a fun and engaging experience while addressing the monotony of traditional rehabilitation methods. The prototype focuses on a single minigame, Flower Picking, which uses gesture recognition to interact with virtual elements, encouraging users to perform exercises that improve hand dexterity. The development process involved creating accessible and user-centered designs using Figma, integrating gesture recognition algorithms, and implementing unity-based game mechanics. Real-time feedback and progressive difficulty levels ensured a personalized experience, motivating users to adhere to rehabilitation routines. The prototype achieved a gesture detection precision of 90%, effectively recognizing predefined gestures such as the Fist and OK symbols. Quantitative analysis highlighted a 40% increase in average session duration compared to traditional exercises, while qualitative feedback praised the app’s immersive design and ease of use. Despite its success, challenges included rigidity in gesture recognition, requiring precise hand orientations, and limited gesture support. Future improvements include expanding gesture adaptability and incorporating additional minigames to target a broader range of exercises. The project demonstrates the potential of gamification in stroke rehabilitation, offering a scalable and accessible solution that complements clinical treatments, making recovery engaging and effective for users.

Keywords: stroke rehabilitation, haptic feedback, gamification, MediaPipe, motor control

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Authors: Ahmed Abdullah Ahmed

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The task of recognizing the writer of a handwritten text has been an attractive research problem in the document analysis and recognition community with applications in handwriting forensics, paleography, document examination and handwriting recognition. This research presents an automatic method for writer recognition from digitized images of unconstrained writings. Although a great effort has been made by previous studies to come out with various methods, their performances, especially in terms of accuracy, are fallen short, and room for improvements is still wide open. The proposed technique employs optimal codebook based writer characterization where each writing sample is represented by a set of features computed from two codebooks, beginning and ending. Unlike most of the classical codebook based approaches which segment the writing into graphemes, this study is based on fragmenting a particular area of writing which are beginning and ending strokes. The proposed method starting with contour detection to extract significant information from the handwriting and the curve fragmentation is then employed to categorize the handwriting into Beginning and Ending zones into small fragments. The similar fragments of beginning strokes are grouped together to create Beginning cluster, and similarly, the ending strokes are grouped to create the ending cluster. These two clusters lead to the development of two codebooks (beginning and ending) by choosing the center of every similar fragments group. Writings under study are then represented by computing the probability of occurrence of codebook patterns. The probability distribution is used to characterize each writer. Two writings are then compared by computing distances between their respective probability distribution. The evaluations carried out on ICFHR standard dataset of 206 writers using Beginning and Ending codebooks separately. Finally, the Ending codebook achieved the highest identification rate of 98.23%, which is the best result so far on ICFHR dataset.

Keywords: off-line text-independent writer identification, feature extraction, codebook, fragments

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Authors: Claudia Pisano

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The Italian shockumentary Mondo cane (1962), directed by Gualtiero Jacopetti, Paolo Cavara, and Franco Prosperi, has often been criticized for its supposed racist and colonialist stances. Several critics consider it a film that proclaims, without explicitly mentioning it, the superiority of the white Euro-American individual over the people who do not belong to white-western societies. This paper proposes a different interpretation of the way in which Mondo cane engages with the discourse of race. Through an analysis of crucial scenes and of the relationship between images and voice-over, and through a comparison between the representation of non-white societies in Mondo cane and in some popular Italian newsreels of the 50s-60s, such as 'La Settimana Incom' and 'Mondo Libero,' the paper argues that Mondo cane debunks the western-white superiority that, according to some critics, the film would promote. The continuous and rapid alternance of scenes set in the western world, for example in Europe or in the United States, and scenes set in exotic countries inhabited by non-white peoples highlights the commonalities between these far-away realities, rather than pointing out the superiority of the white-western one. In addition, the subtle irony employed by the voice-over distances Mondo cane from the newsreels that it much resembles for its documentary style. Mondo cane’s treatment and representation of race is analyzed in the light of the work of Australian Aboriginal anthropologist Aileen Moreton-Robinson, which is based on key concepts such as whiteness and whiteness invisibility. Whiteness is defined as the invisible and omnipresent norm based on which everything that does not belong to the white world is labeled as an odd and inferior 'other.' To overcome racial discrimination, it is necessary to make whiteness visible; that is to say, to deprive it of that aura of normalcy and unquestionable righteousness that surrounds it. This essay argues that Mondo cane participates in the process of making whiteness visible through the confrontation of the white people with the visible 'other'. Because the film shows that the common features on which this confrontation is based are violence and bestiality, the paper suggests that the film does not support the idea of the white world being superior to the non-white; on the contrary, it underlines that the entire world is characterized by the same shocking savagery.

Keywords: irony, race, shockumentary, whiteness, whiteness invisibility

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Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

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Authors: Masami Usui

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Kazuo Ishiguro’s The Remains of the Day (1989) underlines another holocaust, an imprisonment of human life, dignity, and self in the globalizing sphere of the twentieth century. The Remains of the Day delineates the invisible and cruel space of “lost and found” in the postcolonial and post-imperial discourse of this century, that is, the Holocaust. The context of the concentration camp or wartime imprisonment such as Auschwitz is transplanted into the public sphere of modern England, Darlington Hall. The voice is retrieved and expressed by the young journalist and heir of Darlington Hall, Mr. David Cardinal. The new media of journalism is an intruder at Darlington Hall and plays a role in revealing the wrongly-input ideology. “Lost and Found” consists of the private and public retrieved voices. Stevens’ journey in 1956 is a return to the past, especially the period between 1935 and 1936. Lost time is retrieved on his journey; yet lost life cannot be revived entirely in his remains of life. The supreme days of Darlington Hall are the terrifying days caused by the Nazis. Fascism, terrorism, and militarism destroyed the wholesomeness of the globe. Into blind Stevens, both Miss Kenton and Mr. Cardinal bring out the common issue, that is, the political conflicts caused by Nazis. Miss Kenton expresses her own ideas against anti-Semitism regarding the Jewish maids in the crucial time when Sir Oswald Mosley’s Blackshirts organization attacked the Anglo Jews between 1935 and 1936. Miss Kenton’s half-muted statement is reinforced and assured by Cardinal in his mention of the 1934 Olympic Rally threatened by Mosley’s Blackshirts. Cardinal’s invasion of Darlington Hall embodies the increasing tension of international politics related to World War II. Darlington Hall accommodates the crucial political issue that definitely influences the fate of the house, its residents, and the nation itself and that is retrieved in the newly progressive and established media.

Keywords: modern English literature, culture studies, communication, history

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study introduces state-of-the-art techniques for recognizing static and dy-namic patterns and forecasting those challenging activities from multi-fused sensors. Further-more, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, we acquired raw data is filtered with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: artificial intelligence, machine learning, gait analysis, local binary pattern (LBP), statistical features, micro-electro-mechanical systems (MEMS), maximum relevance and minimum re-dundancy (MRMR)

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Authors: Ashiq Hussain, Mahwash Hussain, Zeenat Parveen

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Now a days the demand for broadband service has increased. Due to which the researchers are trying to find a solution to provide a large amount of service. There is a shortage of bandwidth because of the use of downloading video, voice and data. One of the solutions to overcome this shortage of bandwidth is to provide the communication system with passive optical components. We have increased the data rate in this system. From experimental results we have concluded that the quality factor has increased by adding passive optical networks.

Keywords: WDM-PON, optical fiber, BER, Q-factor, eye diagram

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Authors: Rahul Malhotra, Anurag Sharma

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice, and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: FTTH, quad play, play service, access networks, data rate

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Authors: Billel Kenidra, Mohamed Benmohammed

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Data mining technique used in the field of clustering is a subject of active research and assists in biological pattern recognition and extraction of new knowledge from raw data. Clustering means the act of partitioning an unlabeled dataset into groups of similar objects. Each group, called a cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Several clustering methods are based on partitional clustering. This category attempts to directly decompose the dataset into a set of disjoint clusters leading to an integer number of clusters that optimizes a given criterion function. The criterion function may emphasize a local or a global structure of the data, and its optimization is an iterative relocation procedure. The K-Means algorithm is one of the most widely used partitional clustering techniques. Since K-Means is extremely sensitive to the initial choice of centers and a poor choice of centers may lead to a local optimum that is quite inferior to the global optimum, we propose a strategy to initiate K-Means centers. The improved K-Means algorithm is compared with the original K-Means, and the results prove how the efficiency has been significantly improved.

Keywords: microarray data mining, biological pattern recognition, partitional clustering, k-means algorithm, centroid initialization

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.

Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means

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Authors: Yuechao Lei, Lei Zhang

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The interface shear between asphalt overlay and existing rigid airport pavements occurs due to differences in the mechanical properties of materials subjected to aircraft loading. Interlayer contact influences the mechanical characteristics of the asphalt overlay directly. However, the effective interlayer relative displacement obtained accurately using existing displacement sensors of the loading apparatus remains challenging. This study aims to utilize digital image correlation technology to enhance the accuracy of interfacial contact parameters by obtaining effective interlayer relative displacements. Composite structure specimens were prepared, and fixtures for interlayer shear tests were designed and fabricated. Subsequently, a digital image recognition scheme for required markers was designed and optimized. Effective interlayer relative displacement values were obtained through image recognition and calculation of surface markers on specimens. Finite element simulations validated the mechanical response of composite specimens with interlayer shearing. Results indicated that an optimized marking approach using the wall mending agent for surface application and color coding enhanced the image recognition quality of marking points on the specimen surface. Further image extraction provided effective interlayer relative displacement values during interlayer shear, thereby improving the accuracy of interface contact parameters. For composite structure specimens utilizing Styrene-Butadiene-Styrene (SBS) modified asphalt as the tack coat, the corresponding maximum interlayer shear stress strength was 0.6 MPa, and fracture energy was 2917 J/m2. This research provides valuable insights for investigating the impact of interlayer contact in composite pavement structures on the mechanical characteristics of asphalt overlay.

Keywords: interlayer contact, effective relative displacement, digital image correlation technology, composite pavement structure, asphalt overlay

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Authors: Madhuri Goswami

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The matrix of power establishes the hegemonic dominance and supremacy of one group through exercising repression and relegation upon the other. However, the injustice done to any race, ethnicity, or caste has instigated the protest and resistance through various modes -social campaigns, political movements, literary expression and so on. Consequently, the search for identity, the means of claiming it and strive for recognition have evolved as the persistent phenomena all through the world. In the discourse of protest and minority literature, these two discourses -African American and Indian Dalit- surprisingly, share wrath and anger, hope and aspiration, and quest for identity and struggle for self-assertion. African American and Indian Dalit are two geographically and culturally apart communities that stand together on a single platform. This paper has sought to comprehend the form and investigate the formation of identity in general and in the literary work of Toni Morrison and Indian Dalit writing, particular, i.e., Black identity and Dalit identity. The study has speculated two types of identity, namely, individual or self and social or collective identity in the literary province of these marginalized literature. Morrison’s work outsources that self-identity is not merely a reflection of an inner essence; it is constructed through social circumstances and relations. Likewise, Dalit writings too have a fair record of discovery of self-hood and formation of identity, which connects to the realization of self-assertion and worthiness of their culture among Dalit writers. Bama, Pawar, Limbale, Pawde, and Kamble investigate their true self concealed amid societal alienation. The study has found that the struggle for recognition is, in fact, the striving to become the definer, instead of just being defined; and, this striving eventually, leads to the introspection among them. To conclude, Morrison as well as Indian marginalized authors, despite being set quite distant, communicate the relation between individual and community in the context of self-consciousness, self-identification and (self) introspection. This research opens a scope for further research to find out similar phenomena and trace an analogy in other world literatures.

Keywords: identity, introspection, self-access, struggle for recognition

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Authors: Muhammad Maisam Abbas, Salman Tariq, Usama Riaz, Muhammad Tanveer, Humaira Abdul Ghafoor

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Autism Spectrum Disorder (ASD) refers to a developmental disorder that impairs an individual's communication and interaction ability. Individuals feel difficult to read facial expressions while communicating or interacting. Facial Expression Recognition (FER) is a unique method of classifying basic human expressions, i.e., happiness, fear, surprise, sadness, disgust, neutral, and anger through static and dynamic sources. This paper conducts a comprehensive comparison and proposed optimal method for a continued research project—a system that can assist people who have Autism Spectrum Disorder (ASD) in recognizing facial expressions. Comparison has been conducted on three supervised learning algorithms EigenFace, FisherFace, and LBPH. The JAFFE, CK+, and TFEID (I&II) datasets have been used to train and test the algorithms. The results were then evaluated based on variance, standard deviation, and accuracy. The experiments showed that FisherFace has the highest accuracy for all datasets and is considered the best algorithm to be implemented in our system.

Keywords: autism spectrum disorder, ASD, EigenFace, facial expression recognition, FisherFace, local binary pattern histogram, LBPH

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Authors: Jin-Ru Yen, Chi-Che Hsieh

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Up to date, the automated border control system has been used at many airports, which features biometric technology to identify passengers. In spite of its efficiency, failures or extra time could occur sometimes. To improve recognition performance, some scholars proposed the idea of using soft biometrics to support facial recognition systems at checkpoints in airports. The result showed that the efficiency and accuracy are improved. This study aims to explore passengers’ acceptance of soft biometric technology (SBT). We developed a survey to discover factors that affect passengers’ acceptance. An online survey was conducted, and an ANOVA (Analysis of variances) was performed. Our results found that passengers of different genders, ages, education levels, and average monthly incomes do not have significant differences in usage attitude. However, in terms of preferred top style on board and average flying frequency per year, passengers with preferences for wearing T-shirts and less flying frequency tend to have better attitudes toward the SBT. On the other hand, factors such as performance expectancy, social influence, facilitating condition, and hedonic motivation have positive influences on either usage attitude or behavioral intention. Behavioral intention is driven by usage attitude as well.

Keywords: smart airport, biometrics, soft biometric technology, willingness to use

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Authors: Hala Zaghloul, Taymoor Nazmy

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One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Alexandra Sweny

Abstract:

This paper contends with the intersection between the act of witnessing and the act of reading in order to consider the relevance of literary testimony and fiction as tools for postcolonial readings of history. J. M. Coetzee's Waiting for the Barbarians elucidates what Primo Levi deems the 'paradoxical' task of testimony: that suffering can only be fully narrated by the sufferer themselves, whose voice and narrative capacity is often foreclosed by the very extent of their trauma. By examining the fictional Magistrate's position as both a reader and translator of history, this paper posits Waiting for the Barbarians as an ethical command against the appropriation of trauma.

Keywords: ethical criticism, limit-experience, postcolonialism, psychic trauma in literature, testimony

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Authors: Shi-Yu Xu

Abstract:

The top three risk factors that have contributed to building collapses during past earthquake events in Taiwan are: "irregular floor plans or elevations," "insufficient columns in single-bay buildings," and the "weak-story problem." Fortunately, these unsound structural characteristics can be directly identified from the floor plans. However, due to the vast number of old buildings, conducting manual inspections to identify these compromised structural features in all existing structures would be time-consuming and prone to human errors. This study aims to develop an algorithm that utilizes artificial intelligence techniques to automatically pinpoint the structural components within a building's floor plans. The obtained spatial information will be utilized to construct a digital structural model of the building. This information, particularly regarding the distribution of columns in the floor plan, can then be used to conduct preliminary seismic assessments of the building. The study employs various image processing and pattern recognition techniques to enhance detection efficiency and accuracy. The study enables a large-scale evaluation of structural vulnerability for numerous old buildings, providing ample time to arrange for structural retrofitting in those buildings that are at risk of significant damage or collapse during earthquakes.

Keywords: structural vulnerability detection, object recognition, seismic capacity assessment, old buildings, artificial intelligence

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Authors: Brahim Sabir

Abstract:

Psycho-Communication disorders negatively affect the academic curriculum for students in higher education. Thus, understanding these disorders, their causes and effects will give education specialists a tool for the decision, which will lead to the resolution of problems related to the integration of students with Psycho-Communication disorders. It is in this context that a statistical study was conducted, targeting the population object of study, namely Moroccan students. Pathological voice samples were recorded and analyzed acoustically with PRAAT software, in order to build a model that will be the basis for the objective diagnostic.

Keywords: psycho-communication disorders, acoustic analysis, PRAAT

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

Abstract:

In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

Procedia PDF Downloads 457

Authors: Hasan Alrashid

Abstract:

The 2005 Hague Convention seeks to ensure legal certainty and predictability between parties in international business transactions. It harmonies exclusive choice of court agreements at the international level between parties to commercial transactions and to govern the recognition and enforcement of judgments resulting from proceedings based on such agreements to promote international trade and investment. Although the choice of court agreements is significant in international business transactions, the United Arab Emirates refuse to recognise it by Article 24 of the Federal Law No. 11 of 1992 of the Civil Procedure Code. A review of judicial judgments in United Arab Emirates up to the present day has revealed that several cases appeared before the Court in different states of United Arab Emirates regarding the recognition of exclusive choice of court agreements. In all the cases, the courts regarded the exclusive choice of court agreements as a direct assault on state authority and sovereignty and refused categorically to recognize choice of court agreements by refusing to stay proceedings in favor of the foreign chosen court. This has created uncertainty and unpredictability in international business transaction in the United Arab Emirates. In June 2011, the first Gulf Judicial Seminar on Cross-Frontier Legal Cooperation in Civil and Commercial Matters was held in Doha, Qatar. The Permanent Bureau of the Hague Conference attended the conference and invited the states of the Gulf Cooperation Council (GCC) namely, The United Arab Emirates, Bahrain, Saudi Arabia, Oman, Qatar and Kuwait to adopt some of the Hague Conventions, one of which was the Hague Convention on Choice of Court Agreements. One of the recommendations of the conference was that the GCC states should research ‘the benefits of predictability and legal certainty provided by the 2005 Convention on Choice of Court Agreements and its resulting advantages for cross-border trade and investment’ for possible adoption of the Hague Convention. Up to today, no further step has been taken by the any of the GCC states to adapt the Hague Convention nor did they conduct research on the benefits of predictability and legal certainty in international business transactions. This paper will argue that the approach regarding the recognition of choice of court agreements in United Arab Emirates states can be improved in order to help the parties in international business transactions avoid parallel litigation and ensure legal certainty and predictability. The focus will be the uncertainty and gaps regarding the choice of court agreements in the United Arab Emirates states. The Hague Convention on choice of court agreements and the importance of harmonisation of the rules of choice of court agreements at international level will also be discussed. Finally, The feasibility and desirability of recognizing choice of court agreements in United Arab Emirates legal system by becoming a party to the Hague Convention will be evaluated.

Keywords: choice of court agreements, party autonomy, public authority, sovereignty

Procedia PDF Downloads 247