Search results for: speech emotion recognition

Authors: James Beauchemin

Abstract:

Introduction: The incarceration of people with mental illness and substance use disorders is a major public health issue, with social, clinical, and economic implications. Yoga participation has been associated with numerous psychological benefits; however, there is a paucity of research examining impacts of yoga with incarcerated populations. The purpose of this study was to evaluate effectiveness of a six-week yoga intervention on several mental health-related variables, including emotion regulation, body awareness, and warnings of substance relapse among incarcerated females. Methods: This study utilized a pre-post, three-arm design, with participants assigned to intervention, therapeutic community, or general population groups. A between-groups analysis of covariance (ANCOVA) was conducted across groups to assess intervention effectiveness using the Difficulties in Emotion Regulation Scale (DERS), Scale of Body Connection (SBC), and Warnings of Relapse (AWARE) Questionnaire. Results: ANCOVA results for warnings of relapse (AWARE) revealed significant between-group differences F(2, 80) = 7.15, p = .001; np2 = .152), with significant pairwise comparisons between the intervention group and both the therapeutic community (p = .001) and the general population (p = .005) groups. Similarly, significant differences were found for emotional regulation (DERS) F(2, 83) = 10.521, p = .000; np2 = .278). Pairwise comparisons indicated a significant difference between the intervention and general population (p = .01). Finally, significant differences between the intervention and control groups were found for body awareness (SBC) F(2, 84) = 3.69, p = .029; np2 = .081). Between-group differences were clarified via pairwise comparisons, indicating significant differences between the intervention group and both the therapeutic community (p = .028) and general population groups (p = .020). Implications: Study results suggest that yoga may be an effective addition to integrative mental health and substance use treatment for incarcerated women, and contributes to increasing evidence that holistic interventions may be an important component for treatment with this population. Specifically, given the prevalence of mental health and substance use disorders, findings revealed that changes in body awareness and emotion regulation may be particularly beneficial for incarcerated populations with substance use challenges as a result of yoga participation. From a systemic perspective, this proactive approach may have long-term implications for both physical and psychological well-being for the incarcerated population as a whole, thereby decreasing the need for traditional treatment. By integrating a more holistic, salutogenic model that emphasizes prevention, interventions like yoga may work to improve the wellness of this population, while providing an alternative or complementary treatment option for those with current symptoms.

Keywords: yoga, mental health, incarceration, wellness

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Authors: M. L. Anitha, K. A. Radhakrishna Rao

Abstract:

With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

Keywords: biometrics, hand geometry features, inner knuckle print, recognition

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Authors: Jui-Hsueh Hu

Abstract:

This study investigated various models of refusal speech acts among three target groups: French learners of Mandarin Chinese (FM), Taiwanese native Mandarin speakers (TM), and native French speakers (NF). The refusal responses were analyzed in terms of their options, frequencies, and sequences and the contents of their semantic formulas. This study also examined differences in refusal strategies, as determined by social status and social distance, among the three groups. The difficulties of refusal speech acts encountered by FM were then generalized. The results indicated that Mandarin instructors of NF should focus on the different reasons for the pragmatic failure of French learners and should assist these learners in mastering refusal speech acts that rely on abundant cultural information. In this study, refusal policies were mainly classified according to the research of Beebe et al. (1990). Discourse completion questionnaires were collected from TM, FM, and NF, and their responses were compared to determine how refusal policies differed among the groups. This study not only emphasized the dissimilarities of refusal strategies between native Mandarin speakers and second-language Mandarin learners but also used NF as a control group. The results of this study demonstrated that regarding overall strategies, FM were biased toward NF in terms of strategy choice, order, and content, resulting in pragmatic transfer under the influence of social factors such as 'social status' and 'social distance,' strategy choices of FM were still closer to those of NF, and the phenomenon of pragmatic transfer of FM was revealed. Regarding the refusal difficulties among the three groups, the F-test in the analysis of variance revealed statistical significance was achieved for Role Playing Items 13 and 14 (P < 0.05). A difference was observed in the average number of refusal difficulties between the participants. However, after multiple comparisons, it was found that item 13 (unrecognized heterosexual junior colleague requesting contacts) was significantly more difficult for NF than for TM and FM; item 14 (contacts requested by an unrecognized classmate of the opposite sex) was significantly more difficult to refuse for NF than for TM. This study summarized the pragmatic language errors that most FM often perform, including the misuse or absence of modal words, hedging expressions, and empty words at the end of sentences, as the reasons for pragmatic failures. The common social pragmatic failures of FM include inaccurately applying the level of directness and formality.

Keywords: French Mandarin, interlanguage refusal, pragmatic transfer, speech acts

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Authors: Dauda A. Oladosu, Kamorudeen A Olaiya, Abdurahman Bello

Abstract:

The deployment of smart meters by utility providers to gather fine grained spatiotemporal consumption data has grossly influenced the consumers’ emotion and behavior towards energy utilization. The quest for reduction in power consumption is now a subject of concern and one the methods adopted by the consumers to achieve this is Non-intrusive Load (appliance) Monitoring. Hence, this work presents performance Analysis of Domotics System as a tool for load monitoring when integrated with Consumer Control Unit of residential building. The system was developed with basic elements which enhance remote sensing, DTMF (Dual Tone Multi-frequency) recognition and cryptic messaging when specific task was performed. To demonstrate its applicability and suitability, this prototype was used consistently for six months at different load demands and the utilities consumed were documented. The results obtained shows good response when phone dialed, and the packet delivery of feedback SMS was quite satisfactory, making the implemented system to be of good quality with affordable cost and performs the desired functions. Besides, comparative analysis showed notable reduction in energy consumption and invariably lessened electrical bill of the consumer.

Keywords: automation, domotics, energy, load, remote, schedule

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Authors: Robert Long, Hiroaki Watanabe

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This longitudinal study examines the grammatical errors of Japanese university students’ dialogues with a native speaker over an academic year. The L2 interactions of 15 Japanese speakers were taken from the JUSFC2018 corpus (April/May 2018) and the JUSFC2019 corpus (January/February). The corpora were based on a self-introduction monologue and a three-question dialogue; however, this study examines the grammatical accuracy found in the dialogues. Research questions focused on a possible significant difference in grammatical accuracy from the first interview session in 2018 and the second one the following year, specifically regarding errors in clauses per 100 words, global errors and local errors, and with specific errors related to parts of speech. The investigation also focused on which forms showed the least improvement or had worsened? Descriptive statistics showed that error-free clauses/errors per 100 words decreased slightly while clauses with errors/100 words increased by one clause. Global errors showed a significant decline, while local errors increased from 97 to 158 errors. For errors related to parts of speech, a t-test confirmed there was a significant difference between the two speech corpora with more error frequency occurring in the 2019 corpus. This data highlights the difficulty in having students self-edit themselves.

Keywords: clause analysis, global vs. local errors, grammatical accuracy, L2 output, longitudinal study

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Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

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Authors: María de los Ángeles Cerda González, Itzel Arriaga Hurtado, Pascacio José Martínez Pichardo

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In México, the recognition of refugee condition is a fundamental right which, as host State, has the obligation of respect, protect, and fulfill to the foreigners – where we can find the figure of immigrants in irregular situation-, that cannot return to their country of origin for humanitarian reasons. The recognition of the refugee condition as a fundamental right in the Mexican law system proceeds under these situations: 1. The immigrant applies for the refugee condition, even without the necessary proving elements to accredit the humanitarian character of his departure from his country of origin. 2. The immigrant does not apply for the recognition of refugee because he does not know he has the right to, even if he has the profile to apply for. 3. The immigrant who applies fulfills the requirements of the administrative procedure and has access to the refugee recognition. Of the three situations above, only the last one is contemplated for the national indexes of the status refugee; and the first two prove the inefficiency of the governmental system viewed from its lack of sensibility consequence of the no education in human rights matter and which results in the legal vulnerability of the immigrants in irregular situation because they do not have access to the procuration and administration of justice. In the aim of determining the causes and consequences of the no recognition of the refugee status, this investigation was structured from a systemic analysis which objective is to show the advances in Central American humanitarian emergency investigation, the Mexican States actions to protect, respect and fulfil the fundamental right of refugee of immigrants in irregular situation and the social and legal vulnerabilities suffered by Central Americans in Mexico. Therefore, to achieve the deduction of the legal nature of the humanitarian emergency from the Human Rights as a branch of the International Public Law, a conceptual framework is structured using the inductive deductive method. The problem statement is made from a legal framework to approach a theoretical scheme under the theory of social systems, from the analysis of the lack of communication of the governmental and normative subsystems of the Mexican legal system relative to the process undertaken by the Central American immigrants to achieve the recognition of the refugee status as a human right. Accordingly, is determined that fulfilling the obligations of the State referent to grant the right of the recognition of the refugee condition, would mean a guideline for a new stage in Mexican Law, because it would enlarge the constitutional benefits to everyone whose right to the recognition of refugee has been denied an as consequence, a great advance in human rights matter would be achieved.

Keywords: central American immigrants in irregular situation, humanitarian emergency, human rights, refugee

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Authors: Anh Nguyen, Jane Fisher, Thach Tran, Anh T. T. Tran

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Trait Emotional Intelligence is the knowledge, beliefs, and attitudes an individual has about their own and other people’s emotions. It is believed that trait emotional intelligence is a component of personality. Petrides’ Trait Emotional Intelligence Questionnaire (TEIQue) is well regarded and well-established, with validation data about its functioning among adults from many countries. However, there is little data yet about its use among Asian populations, including adolescents. The aims were to translate and culturally verify the Trait Emotional Intelligence Adolescent Short Form (TEIQue-ASF) and investigate content validity, construct validity, and reliability among adolescents attending high schools in Vietnam. Content of the TEIQue-ASF was translated (English to Vietnamese) and back-translated (Vietnamese to English) in consultation with bilingual and bicultural health researchers and pilot tested among 51 potential respondents. Phraseology and wording were then adjusted and the final version is named the VN-TEIQue-ASF. The VN-TEIQue-ASF’s properties were investigated in a cross-sectional elf-report survey among high school students in Central Vietnam. In total 1,546 / 1,573 (98.3%) eligible students from nine high schools in rural, urban, and coastline areas completed the survey. Explanatory Factor Analysis yielded a four-factor solution, including some with facets that loaded differently compared to the original version: Well-being, Emotion in Relationships, Emotion Self-management, and Emotion Sensitivity. The Cronbach’s alpha of the global score for the VN-TEIQue-ASF was .77. The VN-TEIQue-ASF is comprehensible and has good content and construct validity and reliability among adolescents in Vietnam. The factor structure is only partly replicated the original version. The VN-TEIQue-ASF is recommended for use in school or community surveys and professional study in education, psychology, and public health to investigate the trait emotional intelligence of adolescents in Vietnam.

Keywords: adolescents, construct validity, content validity, factor analysis, questionnaire validity, trait emotional intelligence, Vietnam

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Morteza Azimi

Abstract:

The demand for recognition among LGBTQIA+ groups has grown significantly in modern times, particularly since the second half of the twentieth century, when human rights discourse became increasingly prominent, especially in the West. In contrast, the classic readings of the Quran and Hadith, whose roots lie in pre-modern times, and the Shiite Figh (Islamic jurisprudence) seem not to be updated and responsive to the need for recognition by gender minority identities. Moreover, the recognition of such minority identities within Shiite Islam and its intersection with secular frameworks remains an underexplored topic. This paper explores what Islamic texts, such as the Quran, Hadith, and Shiite Fiqh, address regarding the recognition and rights of gender minorities. It further examines the Islamic Republic of Iran as an example of a dominant Shiite political system, comparing it with Turkey and Germany as secular models. While Turkey, a secular state, is deeply influenced by its predominantly Muslim population and culture, Germany represents a Western model characterized by the widespread recognition of LGBTQIA+ rights. The rationale for this comparative approach lies in understanding how different political systems influence the recognition of gender minorities. Moreover, the study investigates whether Shiite Islamic frameworks can provide solutions to these demands or whether secular systems, as exemplified by Turkey and Germany, are more effective in addressing issues of gender minorities. Hence, this study offers a novel perspective by juxtaposing Shiite Islamic textual interpretations with secular legal frameworks to explore the evolving recognition of gender minorities, demonstrating how varying political and cultural contexts shape the lived experiences of LGBTQIA+ individuals in Iran, Turkey, and Germany. This research relies on secondary literature as the primary data source, especially regarding the issue of gender in Shiite Islamic texts. The author employs a comparative textual analysis of Shiite Islamic texts (e.g., Quran, Hadith, and Fiqh) and secular legal frameworks in Turkey and Germany to explore how different systems address the recognition of gender minorities. Findings reveal that classical interpretations of Islamic texts and Shiite Fiqh employed by the Islamic Republic of Iran fail to provide laws and frameworks that recognize LGBTQIA+ identities. This gap contributes to the marginalization of gender minority identities, fostering environments of suppression, violence, and exclusion. The findings of this study could inform policymaking and advocacy efforts by shedding light on the necessity of a change toward inclusive legal and cultural frameworks for gender minorities in Muslim countries like Iran.

Keywords: gender minorities, LGBTQIA+ recognition, shiite islam, comparative analysis

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Authors: B. S. Premalatha, Kausalya Sahani

Abstract:

Background: Glomus jugulare tumors arise within the jugular foramen and are commonly seen in females particularly on the left side. Surgical excision of the tumor may cause lower cranial nerve deficits. Cranial nerve involvement produces hoarseness of voice, slurred speech, and dysphagia along with other physical symptoms, thereby affecting the quality of life of individuals. Though oncological clearance is mainly emphasized on while treating these individuals, little importance is given to their communication, voice and swallowing problems, which play a crucial part in daily functioning. Objective: To examine the functions of voice, speech and swallowing outcomes of the subjects, following excision of glomus jugulare tumor. Methods: Two female subjects aged 56 and 62 years had come with a complaint of change in voice, inability to swallow and reduced clarity of speech following surgery for left glomus jugulare tumor were participants of the study. Their surgical information revealed multiple cranial nerve palsies involving the left facial, left superior and recurrent branches of the vagus nerve, left pharyngeal, left soft palate, left hypoglossal and vestibular nerves. Functional outcomes of voice, speech and swallowing were evaluated by perceptual and objective assessment procedures. Assessment included the examination of oral structures and functions, dysarthria by Frenchey dysarthria assessment, cranial nerve functions and swallowing functions. MDVP and Dr. Speech software were used to evaluate acoustic parameters of voice and quality of voice respectively. Results: The study revealed that both the subjects, subsequent to excision of glomus jugulare tumor, showed a varied picture of affected oral structure and functions, articulation, voice and swallowing functions. The cranial nerve assessment showed impairment of the vagus, hypoglossal, facial and glossopharyngeal nerves. Voice examination indicated vocal cord paralysis associated with breathy quality of voice, weak voluntary cough, reduced pitch and loudness range, and poor respiratory support. Perturbation parameters as jitter, shimmer were affected along with s/z ratio indicative of voice fold pathology. Reduced MPD(Maximum Phonation Duration) of vowels indicated that disturbed coordination between respiratory and laryngeal systems. Hypernasality was found to be a prominent feature which reduced speech intelligibility. Imprecise articulation was seen in both the subjects as the hypoglossal nerve was affected following surgery. Injury to vagus, hypoglossal, gloss pharyngeal and facial nerves disturbed the function of swallowing. All the phases of swallow were affected. Aspiration was observed before and during the swallow, confirming the oropharyngeal dysphagia. All the subsystems were affected as per Frenchey Dysarthria Assessment signifying the diagnosis of flaccid dysarthria. Conclusion: There is an observable communication and swallowing difficulty seen following excision of glomus jugulare tumor. Even with complete resection, extensive rehabilitation may be necessary due to significant lower cranial nerve dysfunction. The finding of the present study stresses the need for involvement of as speech and swallowing therapist for pre-operative counseling and assessment of functional outcomes.

Keywords: functional outcome, glomus jugulare tumor excision, multiple cranial nerve impairment, speech and swallowing

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Sandra Baez, Mariana Pino, Mildred Berrio, Hernando Santamaria-Garcia, Lucas Sedeno, Adolfo Garcia, Sol Fittipaldi, Agustin Ibanez

Abstract:

Schadenfreude refers to the perceiver’s experience of pleasure at another’s misfortune. This is a multidetermined emotion which can be evoked by hostile feelings and envy. The experience of Schadenfreude engages mechanisms implicated in diverse social cognitive processes. For instance, Schadenfreude involves heightened reward processing, accompanied by increased striatal engagement and it interacts with mentalizing and perspective-taking abilities. Patients with Huntington's disease (HD) exhibit reductions of Schadenfreude experience, suggesting a role of striatal degeneration in such an impairment. However, no study has directly assessed the relationship between regional brain atrophy in HD and reduced Schadenfreude. This study investigated whether gray matter (GM) atrophy in HD patients correlates with ratings of Schadenfreude. First, we compared the performance of 20 HD patients and 23 controls on an experimental task designed to trigger Schadenfreude and envy (another social emotion acting as a control condition). Second, we compared GM volume between groups. Third, we examined brain regions where atrophy might be associated with specific impairments in the patients. Results showed that while both groups showed similar ratings of envy, HD patients reported lower Schadenfreude. The latter pattern was related to atrophy in regions of the reward system (ventral striatum) and the mentalizing network (precuneus and superior parietal lobule). Our results shed light on the intertwining of reward and socioemotional processes in Schadenfreude, while offering novel evidence about their neural correlates. In addition, our results open the door to future studies investigating social emotion processing in other clinical populations characterized by striatal or mentalizing network impairments (e.g., Parkinson’s disease, schizophrenia, autism spectrum disorders).

Keywords: envy, Gray matter atrophy, Huntigton's disease, Schadenfreude, social emotions

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Authors: James D. Beauchemin

Abstract:

Introduction: The incarceration of people with mental illness and substance use disorders is a major public health issue with social, clinical, and economic implications. Yoga participation has been associated with numerous psychological benefits; however, there is a paucity of research examining impacts of yoga with incarcerated populations. The purpose of this study was to evaluate effectiveness of a six-week yoga intervention on several mental health-related variables, including emotion regulation, body awareness, and warnings of substance relapse among incarcerated females. Methods: This study utilized a pre-post, three-arm design, with participants assigned to intervention, therapeutic community, or general population groups. A between-group analysis of covariance (ANCOVA) was conducted across groups to assess intervention effectiveness using the Difficulties in Emotion Regulation Scale (DERS), Scale of Body Connection (SBC), and Warnings of Relapse (AWARE) Questionnaire. Results: ANCOVA results for warnings of relapse (AWARE) revealed significant between-group differences F(2, 80) = 7.15, p = .001; np2 = .152), with significant pairwise comparisons between the intervention group and both the therapeutic community (p = .001) and the general population (p = .005) groups. Similarly, significant differences were found for emotional regulation (DERS) F(2, 83) = 10.521, p = .000; np2 = .278). Pairwise comparisons indicated a significant difference between the intervention and general population (p = .01). Finally, significant differences between the intervention and control groups were found for body awareness (SBC) F(2, 84) = 3.69, p = .029; np2 = .081). Between-group differences were clarified via pairwise comparisons, indicating significant differences between the intervention group and both the therapeutic community (p = .028) and general population groups (p = .020). Implications: Study results suggest that yoga may be an effective addition to integrative mental health and substance use treatment for incarcerated women and contributes to increasing evidence that holistic interventions may be an important component for treatment with this population. Specifically, given the prevalence of mental health and substance use disorders, findings revealed that changes in body awareness and emotion regulation might be particularly beneficial for incarcerated populations with substance use challenges as a result of yoga participation. From a systemic perspective, this proactive approach may have long-term implications for both physical and psychological well-being for the incarcerated population as a whole, thereby decreasing the need for traditional treatment. By integrating a more holistic, salutogenic model that emphasizes prevention, interventions like yoga may work to improve the wellness of this population while providing an alternative or complementary treatment option for those with current symptoms.

Keywords: wellness, solution-focused coaching, college students, prevention

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Authors: Tamer Mesallam, Medhat Yousef, Ayna Almasaad

Abstract:

BACKGROUND: Cochlear implantation (CI) in children with additional disabilities can be a fundamental and supportive intervention. Although, there may be some positive impacts of CI on children with multiple disabilities such as better outcomes of communication skills, development, and quality of life, the families of those children complain from the post-implant habilitation efforts that considered as a burden. OBJECTIVE: To investigate the outcomes of CI children with different co-disabilities through using the Meaningful Auditory Integration Scale (MAIS) and the Meaningful Use of Speech Scale (MUSS) as outcome measurement tools. METHODS: The study sample comprised 25 hearing-impaired children with co-disability who received cochlear implantation. Age and gender-matched control group of 25 cochlear-implanted children without any other disability has been also included. The participants' auditory skills and speech outcomes were assessed using MAIS and MUSS tests. RESULTS: There was a statistically significant difference in the different outcomes measure between the two groups. However, the outcomes of some multiple disabilities subgroups were comparable to the control group. Around 40% of the participants with co-disabilities experienced advancement in their methods of communication from behavior to oral mode. CONCLUSION: Cochlear-implanted children with multiple disabilities showed variable degrees of auditory and speech outcomes. The degree of benefits depends on the type of the co-disability. Long-term follow-up is recommended for those children.

Keywords: children with disabilities, Cochlear implants, hearing impairment, language development

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Authors: Anderson Rocha, Pedro Miguel de Figueiredo Dinis Oliveira Gaspar

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Machine vision has been widely used in recent years in agriculture, as a tool to promote the automation of processes and increase the levels of productivity. The aim of this work is the development of a path recognition algorithm based on image processing to guide a terrestrial robot in-between tree rows. The proposed algorithm was developed using the software MATLAB, and it uses several image processing operations, such as threshold detection, morphological erosion, histogram equalization and the Hough transform, to find edge lines along tree rows on an image and to create a path to be followed by a mobile robot. To develop the algorithm, a set of images of different types of orchards was used, which made possible the construction of a method capable of identifying paths between trees of different heights and aspects. The algorithm was evaluated using several images with different characteristics of quality and the results showed that the proposed method can successfully detect a path in different types of environments.

Keywords: agricultural mobile robot, image processing, path recognition, hough transform

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Authors: B. S. Premalatha, Kausalya Sahani

Abstract:

Background: Voice is multidimensional which convey emotion, feelings, and communication. Voice disorders have an adverse effect on the physical, emotional and functional domains of an individual. Self-rating by clients about their voice problem helps the clinicians to plan intervention strategies. Voice handicap index is one such self-rating scale contains 30 questions that quantify the functional, physical and emotional impacts of a voice disorder on a patient’s quality of life. Each subsection has 10 questions. Though adapted and validated versions of VHI are available in other Indian languages but not in Telugu, which is a Dravidian language native to India. It is mainly spoken in Andhra Pradesh and neighbouring states in southern India. Objectives: To adapt and validate the English version of Voice Handicap Index (VHI) into Telugu language and evaluate its internal consistency and clinical validate in Telugu speaking population. Materials: The study carried out in three stages. First stage was a forward translation of English version of VHI, was given to ten experts, who were well proficient in writing and reading Telugu and five speech-language pathologists to translate into Telugu. Second Stage was backward translation where translated version of Telugu was given to a different group of ten experts (who were well proficient in writing and reading Telugu) and five speech-language pathologists who were native Telugu speakers and had good proficiency in Telugu and English. The third stage was an administration of translated version on Telugu to the targeted population. Totally 40 clinical subjects and 40 normal controls served as participants, and each group had 26 males and 14 females’ age range of 20 to 60 years. Clinical group comprised of individuals with laryngectomee with the Tracheoesophageal puncture (n=18), laryngitis (n=11), vocal nodules (n=7) and vocal fold palsy (n=4). Participants were asked to mark of their each experience on a 5 point equal appearing scale (0=never, 1=almost never, 2=sometimes, 3=almost always, 4=always) with a maximum total score of 120. Results: Statistical analysis was made by using SPSS software (22.0.0 Version). Mean, standard deviation and percentage (%) were calculated all the participants for both the groups. Internal consistency of VHI in Telugu was found to be excellent with the consistency scores for all the domains such as physical, emotional and functional are 0.742, 0.934and 0.938. The validity of scores showed a significant difference between clinical population and control group for domains like physical, emotional and functional and total scores. P value found to be less than 0.001( < 0.001). Negative correlation found in age and gender among self-domains such as physical, emotional and functional total scores in dysphonic and control group. Conclusion: The present study indicated that VHI in Telugu is able to discriminate participants having voice pathology from normal populations, which make this as a valid tool to collect information about their voice from the participants.

Keywords: adaptation, Telugu Version, translation, Voice Handicap Index (VHI)

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Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

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Authors: Hami-Ziniman Revital, Ashwall Rachelly

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The conflict resolution realm has developed tremendously during the last half-decade. Three main approaches should be mentioned: an Alternative Dispute Resolution (ADR) suggesting processes such as Arbitration or Interests-based Negotiation was developed as an answer to obligations and rights-based conflicts. The Pragmatic mediation approach focuses on the gap between interests and needs of disputants. The Transformative mediation approach focusses on relations and suits identity-based conflicts. In the current study, we examine the conflictual relations between religious and non-religious Jews in Israel and the impact of three transformative mechanisms: Inter-group recognition, In-group empowerment and Value-based reframing on the relations between the participants. The research was conducted during four facilitated joint mediation classes. A unique finding was found. Using both transformative mechanisms and the Contact Hypothesis criteria, we identify transformation in participants’ relations and a considerable change from anger, alienation, and suspiciousness to an increased understanding, affection and interpersonal concern towards the out-group members. Intergroup Recognition, In-group empowerment, and Values-based reframing were the skills discovered as the main enablers of the change in the relations and the research participants’ fostered mutual recognition of the out-group values and identity-based issues. We conclude this transformation was possible due to a constant intergroup contact, based on the Contact Hypothesis criteria. In addition, as Interests-based mediation uses “Reframing” as a skill to acknowledge both mutual and opposite needs of the disputants, we suggest the use of “Value-based Reframing” in intergroup identity-based conflicts, as a skill contributes to the empowerment and the recognition of both mutual and different out-group values. We offer to implement those insights and skills to assist conflict resolution facilitators in various intergroup identity-based conflicts resolution efforts and to establish further research and knowledge.

Keywords: empowerment, identity-based conflict, intergroup recognition, intergroup relations, mediation skills, multi-cultural society, reframing, value-based recognition

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

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

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

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Authors: Samuel Mwangi, Josephine K. Mule

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Access control as a security technique regulates who or what can access resources. It is a fundamental concept in security that minimizes risks to the institutions that use access control. Regulating access to institutions of higher learning is key to ensure only authorized personnel and students are allowed into the institutions. The use of biometrics has been criticized due to the setup and maintenance costs, hygiene concerns, and trepidations regarding data privacy, among other apprehensions. Facial recognition is arguably a fast and accurate way of validating identity in order to guard protected areas. It guarantees that only authorized individuals gain access to secure locations while requiring far less personal information whilst providing an additional layer of security beyond keys, fobs, or identity cards. This exploratory study sought to investigate the use of facial recognition in controlling access in institutions of higher learning in Kenya. The sample population was drawn from both private and public higher learning institutions. The data is based on responses from staff and students. Questionnaires were used for data collection and follow up interviews conducted to understand responses from the questionnaires. 80% of the sampled population indicated that there were many security breaches by unauthorized people, with some resulting in terror attacks. These security breaches were attributed to stolen identity cases, where staff or student identity cards were stolen and used by criminals to access the institutions. These unauthorized accesses have resulted in losses to the institutions, including reputational damages. The findings indicate that security breaches are a major problem in institutions of higher learning in Kenya. Consequently, access control would be beneficial if employed to curb security breaches. We suggest the use of facial recognition technology, given its uniqueness in identifying users and its non-repudiation capabilities.

Keywords: facial recognition, access control, technology, learning

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Authors: Marc Arthur Bien Aimé, Julio da Silveira Moreira

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This study examines the political speech in Haiti over the course of the decade 2011-2021, focusing on the speeches of the presidents Michel J. Martelly and Jovenel Moïse and their impacts on their awareness collective. In using a qualitative approach, we have analyzed the speech of the president pronounced in response to the political instability of countries, as well as interviews with a group of 20 Haitians living in Port- Au-Prince. Our results put in evidence their complex relationship between politics, awareness collective, and the influence of the powers imperialists. We show that the situation in Haiti's disastrous social and political situation is driven by personal political interests and the absence of a state political project. Moreover, the speeches of the president’s analysis are meaningless, transforming concepts such as social progress and justice in simple words. This political rhetoric contributes to the domination symbolic of the population of Haitian. This study is also linked to the theme “Constitutions, processes democratic and critical of the state in Latin America,” emphasizing the importance of analysis of political speech to understand the complexities of the democratic process and criticism of the State in their Latin American region. We suggest future research to deepen our understanding of these political dynamics and their impact on public policies and developments of the constitutions throughout Latin America.

Keywords: political discourse, conscience collective, inequality social, democratic processes, constitutions, Haiti

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Authors: M. Leonora D. Guerrero

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The study attempted to uncover the most and least frequent phonological variation evident in the speech patterns of grade 1 teachers in select public elementary schools in the Philippines. It also determined the lectal description of the participants based on Tayao’s consonant charts for American and Philippine English. Descriptive method was utilized. A total of 24 grade 1 teachers participated in the study. The instrument used was word list. Each column in the word list is represented by words with the target consonant phonemes: labiodental fricatives f/ and /v/ and lingua-alveolar fricative /z/. These phonemes were in the initial, medial, and final positions, respectively. Findings of the study revealed that the most frequent variation happened when the participants read words with /z/ in the final position while the least frequent variation happened when the participants read words with /z/ in the initial position. The study likewise proved that the grade 1 teachers exhibited the segmental features of both the mesolect and basilect. Based on these results, it is suggested that teachers of English in the Philippines must aspire to manifest the features of the mesolect, if not, the acrolect since it is expected of the academicians not to be displaying the phonological features of the acrolects since this variety is only used by the 'uneducated.' This is especially so with grade 1 teachers who are often mimicked by their students who classify their speech as the 'standard.'

Keywords: consonant phonemes, lectal description, Philippine English, phonological variation

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Authors: Zach Banzon, Marina Caculitan, Gianne Laisac, Stephanie Lopez, Marguerite Villegas

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The role of incidental positive emotions and gender on people’s trust decisions have been established by existing research. The aim of this experiment is to address the gap in the literature by examining whether these factors will have a similar effect on trust behavior even after the experience of betrayal. A total of 144 undergraduate students participated in a trust game involving the anonymous interaction of a participant and a transgressor. Of these participants, only 125 (63 males and 62 females) were included in the data analyses. A story was used to prime incidental positive emotions or emotions originally unrelated to the trustee. Recovered trust was measured by relating the proportion of the money passed before and after betrayal. Data was analyzed using two-way analysis of variance having two levels for gender (male, female) and two for priming (with, without), with trust propensity scores entered as a covariate. It was predicted that trust recovery will be more apparent in females than in males but the data obtained was not significantly different between the genders. Induced positive emotions, however, had a statistically significant effect on trust behavior even after betrayal. No significant interaction effect was found between induced positive emotion and gender. The experiment provides evidence that the manipulation of situational variables, to a certain extent, can facilitate the reparation of trust.

Keywords: gender effect, positive emotions, trust game, trust recovery

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Authors: Shweta Pinjarkar, Shrutika Yawale, Mayuri Patil, Reshma Adagale

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Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application. Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this , demonstrates the face recognition system in android device using eigenface. The system can be used as the base for the development of the recognition of human identity. Test images and training images are taken directly with the camera in android device.The test results showed that the system produces high accuracy. The goal is to implement model for particular face and distinguish it with large number of stored faces. face recognition system detects the faces in picture taken by web camera or digital camera and these images then checked with training images dataset based on descriptive features. Further this algorithm can be extended to recognize the facial expressions of a person.recognition could be carried out under widely varying conditions like frontal view,scaled frontal view subjects with spectacles. The algorithm models the real time varying lightning conditions. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application.

Keywords: face detection, face recognition, eigen faces, algorithm

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Authors: Dina Mahmoud Hammouri

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The study investigates the Jordanian EFL learners’ pragmatic competence through their production of the speech acts of responding to requests, making suggestions, making threats and expressing farewells. The sample of the study consists of 130 Jordanian EFL learners and native speakers. 2600 responses were collected through a Discourse Completion Test (DCT). The findings of the study revealed that the tested students showed similarities and differences in performing the strategies of four speech acts. Differences in the students’ performances led to pragmatic failure instances. The pragmatic failure committed by students refers to a lack of linguistic competence (i.e., pragmalinguistic failure), sociocultural differences and pragmatic transfer (i.e., sociopragmatic failure). EFL learners employed many mechanisms to maintain their communicative competence; the analysis of the test on speech acts showed learners’ tendency towards using particular strategies, resorting to modify strategies and relating them to their grammatical competence, prefabrication, performing long forms, buffing and transfer. The results were also suggestive of the learners’ lack of pragmalinguistic and sociopragmatic knowledge. The implications of this study are for language teachers to teach interlanguage pragmatics explicitly in EFL contexts to draw learners’ attention to both pragmalinguistic and sociopragmatic features, pay more attention to these areas and allocate more time and practice to solve learners’ problems in these areas. The implication of this study is also for pedagogical material designers to provide sufficient and well-organized pragmatic input.

Keywords: pragmatic failure, Jordanian EFL learner, sociopragmatic competence, pragmalinguistic competence

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Authors: Anochao Phetcharat

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This research aims to study problems of translation basic knowledge, particularly from English into Thai. The researcher used 38 2nd-year non-English speaking students of Suratthani Rajabhat University as samples. The samples were required to translate an A4-sized article from English into Thai assigned as a part of BEN0202 Translation for Business, a requirement subject for Business English Department, which was also taught by the researcher. After completion of the translation, numerous problems were found and the research grouped them into 4 major types. The normally occurred problems in English-Thai translation works are the lack of knowledge in terms of parts of speech, word-by-word translation employment, misspellings as well as the poor knowledge in English language structure. However, this research is currently under the process of data analysis and shall be completed by the beginning of August. The researcher, nevertheless, predicts that all the above-mentioned problems, will support the researcher’s hypothesizes, that are; 1) the lack of knowledge in terms of parts of speech causes the mistranslation problem; 2) employing word-by-word translation technique hugely results in the mistranslation problem; 3) misspellings yields the mistranslation problem; and 4) the poor knowledge in English language structure also brings about translation errors. The research also predicts that, of all the aforementioned problems, the following ones are found the most, respectively: the poor knowledge in English language structure, word-by-word translation employment, the lack of knowledge in terms of parts of speech, and misspellings.

Keywords: problem, student, Thai, translation

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Authors: Kilari Nikhil, Ankur Tibrewal, Srinivas Kruthiventi S. S.

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Conventional speaker identification systems often fall short of capturing the diverse variations present in speech data due to fixed-scale architectures. In this research, we propose a CNN-based architecture, USENet, designed to overcome these limitations. Leveraging two key techniques, our approach achieves superior performance on the VoxCeleb 1 Dataset without any pre-training. Firstly, we adopt a U-net-inspired design to extract features at multiple scales, empowering our model to capture speech characteristics effectively. Secondly, we introduce the squeeze and excitation block to enhance spatial feature learning. The proposed architecture showcases significant advancements in speaker identification, outperforming existing methods, and holds promise for future research in this domain.

Keywords: multi-scale feature extraction, squeeze and excitation, VoxCeleb1 speaker identification, mel-spectrograms, USENet

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Authors: El Sayed A. Sharara, A. Tsuji, K. Terada

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Call centers have been expanding and they have influence on activation in various markets increasingly. A call center’s work is known as one of the most demanding and stressful jobs. In this paper, we propose the fatigue detection system in order to detect burnout of call center agents in the case of a neck pain and upper back pain. Our proposed system is based on the computer vision technique combined skin color detection with the Viola-Jones object detector. To recognize the gesture of hand poses caused by stress sign, the YCbCr color space is used to detect the skin color region including face and hand poses around the area related to neck ache and upper back pain. A cascade of clarifiers by Viola-Jones is used for face recognition to extract from the skin color region. The detection of hand poses is given by the evaluation of neck pain and upper back pain by using skin color detection and face recognition method. The system performance is evaluated using two groups of dataset created in the laboratory to simulate call center environment. Our call center agent burnout detection system has been implemented by using a web camera and has been processed by MATLAB. From the experimental results, our system achieved 96.3% for upper back pain detection and 94.2% for neck pain detection.

Keywords: call center agents, fatigue, skin color detection, face recognition

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Authors: Yassine Jamoussi, Ameni Youssfi, Henda Ben Ghezala

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With the growing interest in social networking, the interaction of social actors evolved to a source of knowledge in which it becomes possible to perform context aware-reasoning. The information extraction from social networking especially Twitter and Facebook is one of the problems in this area. To extract text from social networking, we need several lexical features and large scale word clustering. We attempt to expand existing tokenizer and to develop our own tagger in order to support the incorrect words currently in existence in Facebook and Twitter. Our goal in this work is to benefit from the lexical features developed for Twitter and online conversational text in previous works, and to develop an extraction model for constructing a huge knowledge based on actions

Keywords: social networking, information extraction, part-of-speech tagging, natural language processing

Procedia PDF Downloads 305