The analysis of Ethiopian traditional music instrument through indigenous knowledge (kirar, masinko, begena, kebero and washint/flute)

This article aims to explore and analytics about Ethiopian traditional music instrument through indigenous knowledge (kirar, masinko, Begena, kebero and washint/flute). The researcher would have observation and referring the difference documentations. Kirar, and masinko are mostly have purposeful for local music including washint, the others which is Kebero, Begena have use full in the majority time for church purpose. Ethiopia has extended culture, art and indigenous knowledge related to original own music. Their studies have qualitative research design that has descriptive methodology to more exploring the traditional music’s free statement descriptions. Its researcher mainly has providing the descriptive information about the Ethiopian traditional music instrument as analytical finding out

Human activity recognition using a wearable camera

Tesi en modalitat cotutela Universitat Politècnica de Catalunya i Queen Mary, University of London. This PhD Thesis has been developed in the framework of, and according to, the rules of the Erasmus Mundus Joint Doctorate on Interactive and Cognitive Environments EMJD ICE [FPA n° 2010-0012]Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad.Postprint (published version

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Human activity recognition using a wearable camera

Advances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first­ person videos. The proposed features encode discriminant characteristics form magnitude, direction and dynamics of motion estimated using optical flow. M:>reover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variaitons of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classi fiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from first-person videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiplemotion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activiites. Furthermore, we apply cross-domain knowledge transfer between inertial­ based and vision-based approaches for egocentric activity recognition. We propose sparsity weightedcombination of information from different motion modalities and/or streams . Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity.Los avances en tecnologías wearables facilitan la comprensión de actividades humanas utilizando cuando se usan videos grabados en primera persona para una amplia gama de aplicaciones. En esta tesis, proponemos características robustas de movimiento para el reconocimiento de actividades humana a partir de videos en primera persona. Las características propuestas codifican características discriminativas estimadas a partir de optical flow como magnitud, dirección y dinámica de movimiento. Además, diseñamos nuevas características de inercia virtual a partir de video, sin usar sensores inerciales, utilizando el movimiento del centroide de intensidad a través de los fotogramas. Los resultados obtenidos en múltiples bases de datos demuestran que las características inerciales basadas en centroides mejoran el rendimiento de reconocimiento en comparación con grid-based características. Además, proponemos un algoritmo multicapa que codifica las relaciones jerárquicas y temporales entre actividades. La primera capa opera en grupos de características que codifican eficazmente las dinámicas del movimiento y las variaciones temporales de características de apariencia entre múltiples fotogramas utilizando una jerarquía. La segunda capa aprovecha el contexto temporal ponderando las salidas de la jerarquía durante el modelado. Además, diseñamos una técnica de postprocesado para filtrar las decisiones utilizando estimaciones pasadas y la confianza de la estimación actual. Validamos el algoritmo propuesto utilizando varios clasificadores. El modelado temporal muestra una mejora del rendimiento en el reconocimiento de actividades. También investigamos el uso de redes profundas (deep networks) para simplificar el diseño manual de características a partir de videos en primera persona. Proponemos apilar espectrogramas para representar movimientos globales a corto plazo. Estos espectrogramas contienen una representación espaciotemporal de múltiples componentes de movimiento. Esto nos permite aplicar convoluciones bidimensionales para aprender funciones de movimiento. Empleamos long short-term memory recurrent networks para codificar la dependencia temporal a largo plazo entre las actividades. Además, aplicamos transferencia de conocimiento entre diferentes dominios (cross-domain knowledge) entre enfoques inerciales y basados en la visión para el reconocimiento de la actividad en primera persona. Proponemos una combinación ponderada de información de diferentes modalidades de movimiento y/o secuencias. Los resultados muestran que el algoritmo propuesto obtiene resultados competitivos en comparación con existentes algoritmos basados en deep learning, a la vez que se reduce la complejidad

Prevalence of HIV associated neurocognitive deficit among HIV positive people in Ethiopia: a cross sectional study at Ayder Referral Hospital

Background: HIV associated neurocognitive deficit impairs motor activity, neuropsychiatric functioning, daily activity and work activity usually due to the immune suppression effect of the virus. Sub-Saharan region including Ethiopia is the region with the highest burden of HIV. However, a few studies are found on this aspect nationally. This study was aimed at determining the prevalence and the factors associated with cognitive impairment among HIV positive people in Ethiopia who attended Ayder Comprehensive Specialized Hospital.Method: A hospital based cross sectional study was employed on 234 participants selected using systematic random sampling technique. Data was collected thrpugh face-to-face interview, observation and document review. International HIV dementia scale, activity of daily living scale and Hospital Anxiety and Depression scale were used to assess neuro cognitive deficit, activity of daily living, anxiety and depression respectively. The data was analyzed by using SPSS window 20.Result: About 88% of the subjects were receiving highly active antiretroviral therapy. The magnitude of Neuro cognitive deficit was 33.3% (95% CI; 27.7% - 40.6%). Impairment in the activity of daily living was observed on 9.8% of the participants. Besides, 55.6% and 67.1% had anxiety and depressive disorders respectively. Late clinical stage of the illness (AOR= 4.2 (95% CI; 1.19, 14.44)) and impairment in the activity of daily living were significantly associated with neurocognitive deficit (AOR= 7.19 (95% CI; 1.73, 21.83).Conclusion: A higher prevalence of neurocognitive deficit was observed that was related to impaired activity of daily living and being in late stages of the illness. Hence, this should be a strong alarm for early detection of the problem and consistent review of the treatment regimen.Keywords: HIV associated Neurocognitive Deficit, Neuro cognitive deficit, HIV Associated Dementia, Cognitive Impairment, International HIV Dementia Scale, Ethiopi

Political public space in the satire theatre case study on the performance eyayu fenges” Ethiopian satire theater

This article aims to describe about the techniques of make understanding for the space of audience or target group in the satire drama in the stage. The researcher would watch the theater in YouTube and in the stage and also read the script which written by Bereket Belayneh in the type of satire drama, its function in terms of political and social issues. In the addition to the above mentioned these script and play must show the use of satire for political and social criticism for the audience clearly with easy understanding. The other thing that plays show transmitted of the message to the higher officials in the comic entertaining way to the audience of the satire drama. Besides techniques, purpose and different features of satire has investigated from the relevant references. Its researcher mainly has shown the technique of making understand for the audience that have been watched the satire theater on the stage. Bereket Belayneh and Girum Zenebe Eyayu fenges Ethiopian Satire Theater focusing on political satire to find the satire and comic elements used in the humor in order to ridicule the political evils and suggest solutions with in artistically color. The primary sources of this article are Bereket Belayneh and Girum zenebe Eyayu fenges Ethiopian Satire Theater. It’s have used as methodology a qualitative descriptive analysis and have use the document study which is that in library, research digital media sites and the relative relevant material

Analytical Axiology of the Political Background in “Vision of Teodros” Ethiopian Historical Theater

This article aims to describe about the analytical axiology of the political background in ye Tewodros raey historical theatre of Ethiopia. The theatre is the most important of the new generation to teach the Emperor Tewodros II challenges and also great visions of new civilized Ethiopia united. The theatre name also in English vision of Tewodros in Amharic are known ye Tewodros raey. As aspect of axiology have been evaluated the value of vision of Theodros to the audience and the advantages. From the theater an axiology aspect of the political background vision of Theodros that the advantages are to more informative about patriotism, social awareness, concentration, imagination, communication skill, fun, problem solving/conflict resolution, trust, memory, aesthetics appreciation, cooperation/collaboration, empathy and tolerance. These papers have been used document study system as well as have used the qualitative research method to more describe as a theatre reviewing the aspect of axiology philosophical aspects

Human activity recognition using a wearable camera

PhDAdvances in wearable technologies are facilitating the understanding of human activities using first-person vision (FPV) for a wide range of assistive applications. In this thesis, we propose robust multiple motion features for human activity recognition from first-person videos. The proposed features encode discriminant characteristics from magnitude, direction and dynamics of motion estimated using optical flow. Moreover, we design novel virtual-inertial features from video, without using the actual inertial sensor, from the movement of intensity centroid across frames. Results on multiple datasets demonstrate that centroid-based inertial features improve the recognition performance of grid-based features. Moreover, we propose a multi-layer modelling framework that encodes hierarchical and temporal relationships among activities. The first layer operates on groups of features that effectively encode motion dynamics and temporal variations of intra-frame appearance descriptors of activities with a hierarchical topology. The second layer exploits the temporal context by weighting the outputs of the hierarchy during modelling. In addition, a post-decoding smoothing technique utilises decisions on past samples based on the confidence of the current sample. We validate the proposed framework with several classifiers, and the temporal modelling is shown to improve recognition performance. We also investigate the use of deep networks to simplify the feature engineering from firstperson videos. We propose a stacking of spectrograms to represent short-term global motions that contains a frequency-time representation of multiple motion components. This enables us to apply 2D convolutions to extract/learn motion features. We employ long short-term memory recurrent network to encode long-term temporal dependency among activities. Furthermore, we apply cross-domain knowledge transfer between inertial-based and vision-based approaches for egocentric activity recognition. We propose sparsity weighted combination of information from different motion modalities and/or streams. Results show that the proposed approach performs competitively with existing deep frameworks, moreover, with reduced complexity

A comparative analysis of theatre art cuniculumfor undergraduate in Indonesia and Ethiopia.

This research aimed at described: their communality in the theater art curriculum for undergraduate in both of Ethiopia and Indonesia. The main objectives are: (1) Exploring the difference between the theatre art curriculum for undergraduate in of Ethiopia and Indonesia. (2) Discovering the similarities between the curiculum of undergraduate theatre art program of Ethiopia and Indonesia. A descriptive qualitative research method was used for its research. It used simple random sampling method to collect data from theatre art curriculum for undergraduate program ddcuments of Ethiopian and Indonesia. The data were gathered by using two different techniques of data collection consisting of document study and open-ended interviews. Document study is the main data collecting technique and interview was used to support the primary technique. During the document study method the data were gathered in library and privately from the different lecturers and scholars that the relative document such like; books, magazines, journals and research papers. The data consist of two types of curriculum documents which are from Ethiopia and Indonesian theatre afi curricula for undergraduate program. These data was compared to examine the similarities and difference in terms of the number of semesters, course, size of contents and the graduate profiles. The results of the research show that the theatre art curriculum for undergraduate in Indonesia ditTerent from that of Ethiopia. The structures of course affangement, time management, the course content size, a number of courses and credits are a big deference between these two cumicula. Several Indonesian course contents and names are similar to those in Ethiopia but the size of r,vhich are different. The courses offered in Ethiopia curriculum are all in the form of course work, while, in Indonesia students do the aourse work for seven semesters and the last semester is for research. Indonesia has an elective course which is 14 credit hours, and in Ethiopia there is no elective course. With regards to course matters and evaluation methodology, the two countries theatre aft curriculums share similarities in terms of the structure, the content and subjective areas of the program. This research can provide the benefit for multiple stakeholders for revising, preparing, evaluating and as a reference for the next studies in the relevant field