Search results for: automatic recognition of speech

Authors: Nayden Chivarov, Denis Chikurtev, Kaloyan Yovchev, Nedko Shivarov

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This paper represents methods for improving the efficiency and precision of service mobile robot. This robot is used for increasing the quality of life of elderly and disabled people. The key concept of the proposed Intelligent Service Mobile Robot is its easier adaptability to achieve services for a wide range of Elderly or Disabled Person’s needs, by performing different tasks for supporting Elderly or Disabled Persons care. We developed robot autonomous navigation and computer vision systems in order to recognize different objects and bring them to the people. Web based user interface is developed to provide easy access and tele-control of the robot by any device through the internet. In this study algorithms for object recognition and localization are proposed for providing successful object recognition and accuracy in the positioning. Different methods for sending movement commands to the mobile robot system are proposed and evaluated. After executing some experiments to show the results of the research, we can summarize that these systems and algorithms provide good control of the service mobile robot and it will be more useful to help the elderly and disabled persons.

Keywords: service robot, mobile robot, autonomous navigation, computer vision, web user interface, ROS

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Authors: Festa Shabani

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This paper aims at investigating bilingual speech in the International Schools of Prishtina. More particularly, it seeks to analyze bilingual phenomena among adolescent students highly exposed to English with the latter as the language of instruction at school in naturally-occurring conversations within school environment. Adolescence was deliberately chosen since it is regarded as an age when peer influence on language choice is the greatest. Driven by daily unsystematic observation and prior research already undertaken, the hypothesis stated is that Albanian continues to be the dominant language among Prishtina international schools’ students with a lot of code-switched items from the English. Furthermore, they will also use lexical borrowings - words already adapted in the receiving language, from the language they have been in contact with, in their speech often in the lack of existing equivalents in Albanian or for other reasons. This is done owing to the fact that the language of instruction at school is English, and any topic related to the language they have been exposed to will trigger them to use English. Therefore, this needs special attention in an attempt to identify patterns of their speech; in this way, linguistic and socio-pragmatic factors will be considered when analyzing the motivations behind their language choice. Methodology for collecting data include participant systematic observation and tape-recording. While observing them in their natural conversations, the fieldworker also took notes, which helped transcribe details better. The paper starts by raising the question of whether code-switching is occurring among Prishtina International Schools’ students highly exposed to English. The data gathered from students in informal settings suggests that there are well-founded grounds for an affirmative answer. The participants in this study are observed to be code-switching, although showing differences in degree. However, a generalization cannot be made on the basis of the findings except in so far it appears that English has, in turn, became a language to which they turn when identifying with the group when discussing about particular school topics. Particularly, participants seemed to use intra-sentential CS in cases when they seem to find an English expression rather easier than an Albanian one when repeating or emphasizing a point when urged to talk about educational issues with English being their language of instruction, and inter-sentential code-switching, particularly when quoting others. Concerning the grammatical aspect of code-switching, the intrasentential CS is used more than the intersentetial one. Speaking of gender, the results show that there were really no significant differences in regards quantity between male and female participants. However, the slight tendency for men to code switch intrasententially more than women was manifested. Similarly, a slight tendency again for a difference to emerge is on intersentential switching, which contributes 21% to the total number of switches for women, but 11% to the total number of switches for men.

Keywords: Albanian, code-switching contact linguistics, bilingual phenomena, lexical borrowing, English

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Tamara Bastianello, Rachele Ferrari, Marinella Majorano

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The adoption of the syllabic approach in preschool programmes could support and reinforce meta-phonological awareness and literacy skills in children. The introduction of a meta-phonological intervention in preschool could facilitate the transition to primary school, especially for children with learning fragilities. In the present contribution, we want to investigate the efficacy of "Simo-syl" intervention in enhancing emergent literacy skills in children (especially for reading). Simo-syl is a 12 weeks multimedia programme developed for children to improve their language and communication skills and later literacy development in preschool. During the intervention, Simo-syl, an invented character, leads children in a series of meta-phonological games. Forty-six Italian preschool children (i.e., the Simo-syl group) participated in the programme; seventeen preschool children (i.e., the control group) did not participate in the intervention. Children in the two groups were between 4;10 and 5;9 years. They were assessed on their vocabulary, morpho-syntactical, meta-phonological, phonological, and phono-articulatory skills twice: 1) at the beginning of the last year of the preschool through standardised paper-based assessment tools and 2) one week after the intervention. All children in the Simo-syl group took part in the meta-phonological programme based on the syllabic approach. The intervention lasted 12 weeks (three activities per week; week 1: activities focused on syllable blending and spelling and a first approach to the written code; weeks 2-11: activities focused on syllables recognition; week 12: activities focused on vowels recognition). Very few children (Simo-syl group = 21, control group = 9) were tested again (post-test) one week after the intervention. Before starting the intervention programme, the Simo-syl and the control groups had similar meta-phonological, phonological, lexical skills (all ps > .05). One week after the intervention, a significant difference emerged between the two groups in their meta-phonological skills (syllable blending, p = .029; syllable spelling, p = .032), in their vowel recognition ability (p = .032) and their word reading skills (p = .05). An ANOVA confirmed the effect of the group membership on the developmental growth for the word reading task (F (1,28) = 6.83, p = .014, ηp2 = .196). Taking part in the Simo-syl intervention has a positive effect on the ability to read in preschool children.

Keywords: intervention programme, literacy skills, meta-phonological skills, syllabic approach

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Lucas Bublitz, Michael Herdrich

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By achieving the first commercialization approval in San Francisco the Autonomous Driving (AD) industry proves the technology maturity of the SAE L4 AD systems and the corresponding software and hardware stack. This milestone reflects the upcoming phase in the industry, where the focus is now about scaling and supervising larger autonomous vehicle (AV) fleets in different operation areas. This requires an operation framework, which organizes and assigns responsibilities to the relevant AV technology and operation stakeholders from the AV system provider, the Remote Intervention Operator, the MaaS provider and regulatory & approval authority. This holistic operation framework consists of technological, processual, and organizational activities to ensure safe operation for fully automated vehicles. Regarding the supervision of large autonomous vehicle fleets, a major focus is on the continuous field monitoring. The field monitoring approach must reflect the safety and security criticality of incidents in the field during driving operation. This includes an automatic containment approach, with the overall goal to avoid safety critical incidents and reduce downtime by a malfunction of the AD software stack. An End-to-end (E2E) field monitoring approach detects critical faults in the field, uses a knowledge-based approach for evaluating the safety criticality and supports the automatic containment of these E/E faults. Applying such an approach will ensure the scalability of AV fleets, which is determined by the handling of incidents in the field and the continuous regulatory compliance of the technology after enhancing the Operational Design Domain (ODD) or the function scope by Functions on Demand (FoD) over the entire digital product lifecycle.

Keywords: field monitoring, incident management, multicompliance management for AI in AD, root cause analysis, database approach

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Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Miguel Contreras, David Long, Will Bachman

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Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

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Authors: Wei Liu, Xionghui Zhou, Qiang Niu, Yunhao Ni

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3D part library is an ideal approach to reuse the existing design and thus facilitates the modeling process, which will enhance the efficiency. In this paper, we implemented the thought on the SolidWorks platform. The system supports the functions of type and parameter selection, 3D template driving and part assembly. Finally, BOM is exported in Excel format. Experiment shows that our method can satisfy the requirement of die and mold designers.

Keywords: part library, SolidWorks, automatic assembly, intelligent

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Authors: Ashish Agarwal, Vaibhav Singh

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Harmful emissions from oil and gas processing facilities have long remained a matter of concern for governments and environmentalists throughout the world. This paper analyses Oil and Natural Gas Corporation (ONGC) gas processing plant in Hazira, Gujarat, India. It is the largest gas-processing complex in the country designed to process 41MMSCMD sour natural gas & associated sour condensate. The complex, sprawling over an area of approximate 705 hectares is the mother plant for almost all industries at Hazira and enroute Hazira Bijapur Jagdishpur pipeline. Various sources of pollution from each unit starting from Gas Terminal to Dew Point Depression unit and Caustic Wash unit along the processing chain were examined with the help of different emission data obtained from ONGC. Pollution discharged to the environment was classified into Water, Air, Hazardous Waste and Solid (Non-Hazardous) Waste so as to analyze each one of them efficiently. To protect air environment, Sulphur recovery unit along with automatic ambient air quality monitoring stations, automatic stack monitoring stations among numerous practices were adopted. To protect water environment different effluent treatment plants were used with due emphasis on aquaculture of the nearby area. Hazira plant has obtained the authorization for handling and disposal of five types of hazardous waste. Most of the hazardous waste were sold to authorized recyclers and the rest was given to Gujarat Pollution Control Board authorized vendors. Non-Hazardous waste was also handled with an overall objective of zero negative impact on the environment. The effect of methods adopted is evident from emission data of the plant which was found to be well under Gujarat Pollution Control Board limits.

Keywords: sulphur recovery unit, effluent treatment plant, hazardous waste, sour gas

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Han Bai, Lemei Yu, Tong Zhang, Doudou Xie, Liang Zhao

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The brightness changes at highway tunnel entrance and exit have an effect on the physical and psychological conditions of drivers. It is more conducive for examining driving safety with quantitative analysis of the physical and psychological characteristics of drivers to determine the speed limit sign locations at the tunnel entrance and exit sections. In this study, the physical and psychological effects of tunnels on traffic sign recognition of drivers are analyzed; subsequently, experiments with the assistant of Eyelink-II Type eye movement monitoring system are conducted in the typical tunnels in Ji-Qing freeway and Xi-Zha freeway, to collect the data of eye movement indexes “Fixation Duration” and “Eyeball Rotating Speed”, which typically represent drivers' mental load and visual characteristics. On this basis, the paper establishes a visual recognition model for the speed limit signs at the highway tunnel entrances and exits. In combination with related standards and regulations, it further presents the recommended values for locating speed limit signs under different tunnel conditions. A case application on Panlong tunnel in Ji-Qing freeway is given to generate the helpful improvement suggestions.

Keywords: driver psychological load, eye movement index, speed limit sign location, tunnel entrance and exit

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Authors: Shang-Yu Chiang, Yu-Shan Tsai, Shih-Hsien Sung, Chung-Ming Lo

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Mitral regurgitation (MR) is a heart disorder which the mitral valve does not close properly when the heart pumps out blood. MR is the most common form of valvular heart disease in the adult population. The diagnostic echocardiographic finding of MR is straightforward due to the well-known clinical evidence. In the determination of MR severity, quantification of sonographic findings would be useful for clinical decision making. Clinically, the vena contracta is a standard for MR evaluation. Vena contracta is the point in a blood stream where the diameter of the stream is the least, and the velocity is the maximum. The quantification of vena contracta, i.e. the vena contracta width (VCW) at mitral valve, can be a numeric measurement for severity assessment. However, manually delineating the VCW may not accurate enough. The result highly depends on the operator experience. Therefore, this study proposed an automatic method to quantify VCW to evaluate MR severity. Based on color Doppler ultrasound, VCW can be observed from the blood flows to the probe as the appearance of red or yellow area. The corresponding brightness represents the value of the flow rate. In the experiment, colors were firstly transformed into HSV (hue, saturation and value) to be closely align with the way human vision perceives red and yellow. Using ellipse to fit the high flow rate area in left atrium, the angle between the mitral valve and the ultrasound probe was calculated to get the vertical shortest diameter as the VCW. Taking the manual measurement as the standard, the method achieved only 0.02 (0.38 vs. 0.36) to 0.03 (0.42 vs. 0.45) cm differences. The result showed that the proposed automatic VCW extraction can be efficient and accurate for clinical use. The process also has the potential to reduce intra- or inter-observer variability at measuring subtle distances.

Keywords: mitral regurgitation, vena contracta, color doppler, image processing

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Authors: Mira Kaneva

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The ongoing migration crisis polemic opens up the debate to the ambivalent essence of borders due to both the legality and legitimacy of the displacement of vast masses of people across the European continent. In neoliberal terms, migration is seen as an economic opportunity, or, on the opposite, as a social disparity; in realist terms, it is regarded as a security threat that calls for mobilization; from a critical standpoint, it is a matter of discourse on democratic governance. This paper sets the objective of analyzing borders through the Foucauldian prism of biopolitics. It aims at defining the specifics of the management of the human body by producing both the irregular migrant as a subject (but prevalently as an object in the discourse) and the political subjectivity by exercising state power in repressive practices, including hate speech. The study relies on the conceptual framework of Bigo, Agamben, Huysmans, among others, and applies the methodology of qualitative comparative analysis between the cases of borders (fences, enclaves, camps and other forms of abnormal spatiality) in Italy, Spain, Greece, the Republic of Macedonia, Serbia and Bulgaria. The paper thus tries to throw light on these cross- and intra-regional contexts that share certain similarities and differences. It tries to argue that the governmentality of the masses of refugees and economic immigrants through the speech acts of their exclusion leads to a temporary populist backlash; a tentative finding is that the status-quo in terms of social and economic measures remains relatively balanced, whereas, values such as freedom, openness, and tolerance are consecutively marginalized.

Keywords: Balkans, biopolitical borders, cross- and intra-regional discourse analysis, irregular migration, Mediterranean Europe, securitization vs. humanitarianism

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Authors: Mahshid Asadollahi, Mohammad Akbari Asl

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The restaurant industry is highly competitive, and restaurants are constantly looking for ways to attract new customers and retain their existing ones. Nonverbal communication is an important factor in creating a positive customer experience and can play a significant role in attracting customers to restaurants. Nonverbal communication can include body language, facial expressions, tone of voice, and physical proximity, among other things. The present study aimed to investigate the impact of nonverbal communication between restaurant employees and customers on attracting customers in food courts in Tehran. The research method was descriptive-correlational, and the statistical population of this study included all customers of food court restaurants in Tehran, which was about 30 restaurants. The research sample was selected through probability sampling, and 440 customers completed emotional response, customer satisfaction, and nonverbal communication questionnaires in person. The data obtained were analyzed using multiple regression analysis. The results showed that vocal language, employee proximity, physical appearance, and speech movements, as components of nonverbal communication of restaurant employees, had an impact on attracting customers. Additionally, positive and negative emotions of customers have a significant relationship with customer attraction in Food Court restaurants. The study shows that various nonverbal communication factors can play a significant role in attracting customers, and that positive and negative customer emotions can affect customer satisfaction. Therefore, restaurant owners and managers should pay attention to nonverbal communication and train their employees accordingly to create a positive and welcoming atmosphere for customers.

Keywords: verbal language, proximity of employees, physical appearance, speech gestures, nonverbal communication, customer emotions, customer attraction

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Authors: Purnima Anjali Mohanty, Mousumi Padhi

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Background of the study: In the background of the passage of the Transgender Bill 2016, which is the first such step of formal recognition of the rights of transgender, the Hijras have been recognized under the wider definition of Transgender. Fascinatingly, in the Hindu social context, Hijras have a long social standing during marriages and childbirths. Other than this ironically, they live an ostracized life. The Bill rather than recognizing their unique characteristics and needs, reinforces the societal dualism through a parallelism of their legal rights with rights available to women. Purpose of the paper: The research objective was to probe why and to what extent did they identify themselves with the feminine gender roles. Originality of the paper: In the Indian context, the subject of eunuch has received relatively little attention. Among the studies that exist, there has been a preponderance of studies from the perspective of social exclusion, rights, and physical health. There has been an absence of research studying the self-identity of Hijras from the gender perspective. Methodology: The paper adopts the grounded theory method to investigate and discuss the underlying gender identity of transgenders. Participants in the study were 30 hijras from various parts of Odisha. 4 Focus group discussions were held for collecting data. The participants were approached in their natural habitat. Following the methodological recommendations of the grounded theory, care was taken to select respondents with varying experiences. The recorded discourses were transcribed verbatim. The transcripts were analysed sentence by sentence, and coded. Common themes were identified, and responses were categorized under the themes. Data collected in the latter group discussions were added till saturation of themes. Finally, the themes were put together to prove that despite the demand for recognition as third gender, the eunuchs of Odisha identify themselves with the feminine roles. Findings: The Hijra have their own social structure and norms which are unique and are in contrast with the mainstream culture. These eunuchs live and reside in KOTHIS (house), where the family is led by a matriarch addressed as Maa (mother) with her daughters (the daughters are eunuchs/effeminate men castrated and not castrated). They all dress up as woman, do womanly duties, expect to be considered and recognized as woman and wife and have the behavioral traits of a woman. Looking from the stance of Feminism one argues that when the Hijras identify themselves with the gender woman then on what grounds they are given the recognition as third gender. As self-identified woman; their claim for recognition as third gender falls flat. Significance of the study: Academically it extends the study of understanding of gender identity and psychology of the Hijras in the Indian context. Practically its significance is far reaching. The findings can be used to address legal and social issues with regards to the rights available to the Hijras.

Keywords: feminism, gender perspective, Hijras, rights, self-identity

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Authors: Israa A. Qari

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Learning to request in a foreign language is a key ability within pragmatics language teaching. This paper examines how requests are taught in English Unlimited Book 3 (Cambridge University Press), an EFL textbook series employed by King Abdulaziz University in Jeddah, Saudi Arabia to teach advanced foundation year students English. The focus of analysis is the evaluation of the request linguistic strategies present in the textbook, frequency of the use of these strategies, and the contextual information provided on the use of these linguistic forms. The researcher collected all the linguistic forms which consisted of the request speech act and divided them into levels employing the CCSARP request coding manual. Findings demonstrated that simple and commonly employed request strategies are introduced. Looking closely at the exercises throughout the chapters, it was noticeable that the book exclusively employed the most direct form of requesting (the imperative) when giving learners instructions: e.g. listen, write, ask, answer, read, look, complete, choose, talk, think, etc. The book also made use of some other request strategies such as ‘hedged performatives’ and ‘query preparatory’. However, it was also found that many strategies were not dealt with in the book, specifically strategies with combined functions (e.g. possibility, ability). On a sociopragmatic level, a strong focus was found to exist on standard situations in which relations between the requester and requestee are clear. In general, contextual information was communicated implicitly only. The textbook did not seem to differentiate between formal and informal request contexts (register) which might consequently impel students to overgeneralize. The paper closes with some recommendations for textbook and curriculum designers. Findings are also contrasted with previous results from similar body of research on EFL requests.

Keywords: EFL, requests, saudi, speech acts, textbook evaluation

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Authors: Ruchika Sharma

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In the past decade, there has been an explosion of research that has examined children’s understanding of TV advertisements and its persuasive intent, socialization of child consumer and child psychology. However, it is evident from the literature review that no studies in this area have covered advertising messages and its impact on children’s brand recall and ad recognition. Copywriters use various creative devices to lure the consumers and very impressionable consumers such as children face far more drastic effects of these creative ways of persuasion. On the basis of Piaget’s theory of cognitive development as a theoretical basis for predicting/understanding children’s response and understanding, a quasi-experiment was carried out for the study, that manipulated measurement timing and advertising messages (familiar vs. unfamiliar) keeping gender and age group as two prominent factors. This study also examines children’s understanding of Advertisements and its elements, predominantly - Language, keeping in view Fishbein’s model. Study revealed significant associations between above mentioned factors and children’s brand recall and ad identification. Further, to test the reliability of the findings on larger sample, bootstrap simulation technique was used. The simulation results are in accordance with the findings of experiment, suggesting that the conclusions obtained from the study can be generalized for entire children’s (as consumers) market in India.

Keywords: advertising, brand recall, cognitive development, preferences

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Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

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Authors: Gunjita Shami, Noopur Anand

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The pilot run of 50 days was undertaken to test employability potential of people with visual and hearing & speech impairment. Various roles in an apparel manufacturing set up like spreading of fabric for cutting, folding, sealing and labeling cartons, pasting size barcode stickers on packed garments, removing tickets from the garments in the finishing stage were studied. Their performance was quantified basis timesheets for all the days and improvement per day was quantified. Their final day output was compared to that of the able-bodied worker. For example in the carton making activity on day one visually impaired worker was making one box every three minutes which improved to four boxes per minute on day 28 displaying 91.6% improvement compared or an improvement of 3.6% per day which was comparable to the able-bodied seasoned workers, who were making 5 boxes per minute. The performance of persons with hearing and speech impairment in the finishing department was 10% higher than that of able-bodied seasoned workers in the same process. Overall in all the activities the differently abled showed day to day improvement of 65% while able bodied displayed improvement of 52%. On the first day performance of able-bodied worker was 75% better than that of differently abled while on the 50th day it was only 20% better. Therefore the performance of persons with disabilities was found comparable to the able bodied person. The results, though on a small scale, showed a big promise of employment of persons with disability in the apparel industry. Armed with the promising result a full-scale study has been undertaken to identify the roles suitable for certain kind of disability in apparel production, work-aids required to assist the differently abled to improve performance and measures to be undertaken to make production floor 'friendlier' for them. The results have been discussed in this paper which opens doors for integrating differently abled into the world projected and assumed for only able-bodied.

Keywords: apparel sector, differently abled, employability, performance, work-aid

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Authors: Tayyaba Azim, Bibi Amina

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The increase in the popularity of opinion mining with the rapid growth in the availability of social networks has attracted a lot of opportunities for research in the various domains of Sentiment Analysis and Natural Language Processing (NLP) using Artificial Intelligence approaches. The latest trend allows the public to actively use the internet for analyzing an individual’s opinion and explore the effectiveness of published facts. The main theme of this research is to account the public opinion on the most crucial and extensively discussed development projects, China Pakistan Economic Corridor (CPEC), considered as a game changer due to its promise of bringing economic prosperity to the region. So far, to the best of our knowledge, the theme of CPEC has not been analyzed for sentiment determination through the ML approach. This research aims to demonstrate the use of ML approaches to spontaneously analyze the public sentiment on Twitter tweets particularly about CPEC. Support Vector Machine SVM is used for classification task classifying tweets into positive, negative and neutral classes. Word2vec and TF-IDF features are used with the SVM model, a comparison of the trained model on manually labelled tweets and automatically generated lexicon is performed. The contributions of this work are: Development of a sentiment analysis system for public tweets on CPEC subject, construction of an automatic generation of the lexicon of public tweets on CPEC, different themes are identified among tweets and sentiments are assigned to each theme. It is worth noting that the applications of web mining that empower e-democracy by improving political transparency and public participation in decision making via social media have not been explored and practised in Pakistan region on CPEC yet.

Keywords: machine learning, natural language processing, sentiment analysis, support vector machine, Word2vec

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Jinwon Kang, Seonghak Jo, Joohee Ahn, Junghye Choi, Sun-Young Lee

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A morphological priming has proved its importance by showing that segmentation occurs in morphemes when visual words are recognized within a noticeably short time. Regarding Sino-Korean prefixes, this study conducted an experiment on visual masked priming tasks with 57 ms stimulus-onset asynchrony (SOA) to see how individual differences in the amount of morphological knowledge affect morphological priming. The relationship between the prime and target words were classified as morphological (e.g., 미개척 migaecheog [unexplored] – 미해결 mihaegyel [unresolved]), semantical (e.g., 친환경 chinhwangyeong [eco-friendly]) – 무공해 mugonghae [no-pollution]), and orthographical (e.g., 미용실 miyongsil [beauty shop] – 미확보 mihwagbo [uncertainty]) conditions. We then compared the priming by configuring irrelevant paired stimuli for each condition’s control group. As a result, in the behavioral data, we observed facilitatory priming from a group with high morphological knowledge only under the morphological condition. In contrast, a group with low morphological knowledge showed the priming only under the orthographic condition. In the event-related potential (ERP) data, the group with high morphological knowledge presented the N250 only under the morphological condition. The findings of this study imply that individual differences in morphological knowledge in Korean may have a significant influence on the segmental processing of Korean word recognition.

Keywords: ERP, individual differences, morphological priming, sino-Korean prefixes

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Authors: Paul D. Facey, Claire Morgan

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Speech anxiety is thought to be deep-seated within the human evolutionary lineage.As a result, almost all people display high levels of anxiety when asked to communicate in front of an audience.However, proficiency in oral communication is considered as an essential skill for a graduate career and significant emphasis is placed on developing these skills in many degree programs.Because of this, many degree schemes incorporate some form of assessed dialogic presentation. Yet, a student’s anxiety over public speaking, especially if severe, can be so great that at worst it can cause the student to withdraw from their study. This study investigated how students perceive their own levels of anxiety when faced with public speaking using the Personal Report of Public Speaking Anxiety (PRPSA) questionnaire developed by McCroskey. Additionally, students were asked to provide examples of adjustments that could be implemented that they felt would alleviate some/all of their anxiety. The results of the study indicated that the majority of the students experienced a moderate level of anxiety. However, further analysis showed that of those who were in the moderate anxiety’ group, 43% fell into the higher range suggesting that overall more students experience higher levels of anxiety when faced with public speaking than maybe first envisaged. Thus, it is essential that steps are taken to address student anxiety in order that students engage with presentations, are motivated and encouraged and do not avoid such assignments. The feedback from our students indicated a need to implement systematic desensitization programs where students learn to overcome their anxiety through a series of sessions that gradually increase their anxiety levels. Furthermore, these sessions should be run in parallel with skills sessions in order for students to be better prepared and allow self-reflection and self-analysis.This study highlights the paucity of these sessions on many degree schemes and suggests that they should form an integral part of a students’ early academic learning.

Keywords: student anxiety, communication anxiety, public speaking, higher education, desensitisation

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Authors: Raúl Jiménez Vilches

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Native (L1) speakers can mark prosodically one part of an utterance and make it more relevant as opposed to the rest of the constituents. Conversely, non-native (L2) speakers encounter problems when it comes to marking prosodically information structure in English. In fact, the L2 speaker’s choice for the prosodic realization of focus is not so clear and often obscures the intended pragmatic meaning and the communicative value in general. This paper reports some of the findings obtained in an L2 prosodic training course for Spanish learners of English within the context of public speaking. More specifically, it analyses the effects of the course experiment in relation to the non-native production of the tonic syllable to mark focus and compares it with the public speeches delivered by native English speakers. The whole experimental training was executed throughout eighteen input sessions (1,440 minutes total time) and all the sessions took place in the classroom. In particular, the first part of the course provided explicit instruction on the recognition and production of the tonic syllable and how the tonic syllable is used to express focus. The non-native and native oral presentations were acoustically analyzed using Praat software for speech analysis (7,356 words in total). The investigation adopted mixed and embedded methodologies. Quantitative information is needed when measuring acoustically the phonetic realization of focus. Qualitative data such as questionnaires, interviews, and observations were also used to interpret the quantitative data. The embedded experiment design was implemented through the analysis of the public speeches before and after the intervention. Results indicate that, even after the L2 prosodic training course, Spanish learners of English still show some major inconsistencies in marking focus effectively. Although there was occasional improvement regarding the choice for location and word classes, Spanish learners were, in general, far from achieving similar results to the ones obtained by the English native speakers in the two types of focus. The prosodic realization of focus seems to be one of the hardest areas of the English prosodic system to be mastered by Spanish learners. A funded research project is in the process of moving the present classroom-based experiment to an online environment (mobile app) and determining whether there is a more effective focus usage through CAPT (Computer-Assisted Pronunciation) tools.

Keywords: focus, prosody, public speaking, Spanish learners of English

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: image processing, illumination equalization, shadow filtering, object detection

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Authors: Gülten Silindir Keretli

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The aim of this study is to analyse the works of playwrights within the framework of existential philosophy. It is to observe the ontological existence in the plays of No Exit and Endgame. Literary works will be discussed separately in each section of this study. The despair of post-war generation of Europe problematized the ‘human condition’ in every field of literature which is the very product of social upheaval. With this concern in his mind, Sartre’s creative works portrayed man as a lonely being, burdened with terrifying freedom to choose and create his own meaning in an apparently meaningless world. The traces of the existential thought are to be found throughout the history of philosophy and literature. On the other hand, the theatre of the absurd is a form of drama showing the absurdity of the human condition and it is heavily influenced by the existential philosophy. Beckett is the most influential playwright of the theatre of the absurd. The themes and thoughts in his plays share many tenets of the existential philosophy. The existential philosophy posits the meaninglessness of existence and it regards man as being thrown into the universe and into desolate isolation. To overcome loneliness and isolation, the human ego needs recognition from the other people. Sartre calls this need of recognition as the need for ‘the Look’ (Le regard) from the Other. In this paper, existentialist philosophy and existentialist angst will be elaborated and then the works of existentialist theatre and theatre of absurd will be discussed within the framework of existential philosophy.

Keywords: consciousness, existentialism, the notion of the absurd, the other

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Authors: Joe Voshall, Leigha Shoup

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In 2022, the Ohio Peace Officer Training Council and the Attorney General required officers to complete a minimum of 24 hours of continued professional training for the year. Much of the training was based on Mental Health or similarly related topics. This includes Officer Wellness and Officer Mental Health. It is becoming clearer that the stigma of Officer / First Responder Mental Health is a topic that is becoming more prevalently faced. To assist officers and first responders in facing mental health issues, we are developing new training. This training will aid in recognizing mental health-related issues in officers/first responders and citizens, as well as further using the same information to better respond and interact with one another and the public. In general, society has many varying views of mental health, much of which is largely over-sensationalized by television, movies, and other forms of entertainment. There has also been a stigma in law enforcement / first responders related to mental health and being weak as a result of on-the-job-related trauma-induced struggles. It is our hope this new training will assist officers and first responders in not only positively facing and addressing their mental health but using their own experience and education to recognize signs and symptoms of mental health within individuals in the community. Further, we hope that through this recognition, officers and first responders can use their experiences and more in-depth understanding to better interact within the field and with the public. Through recognition and better understanding of mental health issues and more positive interaction with the public, additional achievements are likely to result. This includes in the removal of bias and stigma for everyone.

Keywords: law enforcement, mental health, officer related mental health, trauma

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Authors: Kirsten A. Bartels

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There are a number of similarities between survivor literature and Holocaust fiction for children and young adults. The paper explores three facets of the parallels of recognition found specifically between Livia Bitton-Jackson’s memoir of her experience during the Holocaust as an inmate in Auschwitz, I Have Lived a Thousand Years (1999) and Morris Glietzman series of Holocaust fiction. While Bitton-Jackson reflects on her past and Glietzman designs a fictive character, both are judicious with what they are willing to impart, only providing information about their appearance or themselves when it impacts others or when it serves a necessary purpose to the story. Another similarity lies in another critical aspect of many works of Holocaust literature – the idea of being ‘representatively Jewish’. The authors come to this idea from different angles, perhaps best explained as the difference between showing and telling, for Bitton-Jackson provides personal details, and Gleitzman constructed Felix arguably with this idea in mind. Interwoven through their journeys is a shift in perspectives on being recognized -- from wanting to be seen as individuals to being seen as Jew. With this, being Jewish takes on different meaning, both youths struggle with being labeled as something they do not truly understand, and may have not truly identified with, from a label, to a death warrant. With survivor literature viewed as the most credible and worthwhile type of Holocaust literature and Holocaust fiction is often seen as the least (with children’s and young-adult being the lowest form) the similarities in approaches to telling the stories may go overlooked or be undervalued. This paper serves as an exploration in the some of parallel messages shared between the two.

Keywords: holocaust fiction, Holocaust literature, representatively Jewish, survivor literature

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