Research Methodology - Abstract A

  1. The research question that has been mainly attempted by the researcher to answer is: What are the factors that influence of social network services acceptance among the users.
  2. There is a secondary research question that accompanies the main research question. The secondary research question which has been included is how the perception of the users about a target social network service as a task based service or a relationship based service can be referred as a moderator between the constructs and actual use.
  3. The current authors have attempted to develop an amended model with the application of “Technology Acceptance Model (TAM)” so that the characteristics of the users that influence their choice or acceptance regarding social network services (Gauld et al., 2017). This model is referred to an information system theory that helps in studying how a user become influenced to start accepting and using a technology. In order to develop a behavioural form that influences individual to actually use the system and increase the ability to use those technology to form a behavioural intention.
  4. Finding out the answers to the research questions developed authors have come to conclude that the constructs that significantly is responsible for affecting or influencing individual for actual use of the social network services are the “perceived orientation” and “perceived encouragement”.
  5. The authors would have used the survey method for collecting the data. As the current authors are studying the perception of the individuals and their personal characteristics that are able to influence the acceptance of the users of social network service, survey could have been an ideal decision to conduct (Bulling & Kunze, 2016). It is because perception of the individual users is not understandable without the participation of human respondents. Apart from that development of perception of the users is dependent upon the time span they are using social network services. Therefore, individuals that are using social network services heavily would have been selected as sample of the study.

Apart from the data collection technique used by the current authors, a method consisting of both the primary and secondary research would be used as it is required to understand in-depth that what the current literature suggests about the current research concern.


H1- There is a significant relationship between user acceptance of social network services and perceived usefulness

H2- There is a significant relationship between user acceptance of social network services and Actual use

As the statistical analysis, the results could be measured through conducting a correlation test among the factors of the measurement model and the variable studied as the dependent variable.

Perceived encouragement is referred to the shared perception of a group of a user that is communicated through frequency and effectiveness. Perceived orientation is the perception of the individual users regarding the referral. These two factors are responsible for influencing the users to accept the social network service.

The research’s internal validation is low as the behavioural examination is not controlled by the researchers and it can change over the changes in social network uses and concerns. This research is high in its External validity as the data collection has been conducted in a practical and implementing manner.

Research Methodology - Abstract B

  1. The main question is how the pedestrian body can be described by exploiting the “depth voxel covariance descriptor.”
  2. Another question is how the invariant depth feature that is a locally rotation invariant depth shape descriptor can be used to describe the pedestrian body.
  3. The authors have proposed to use the depth information so that invariant body shape can be provided along with the information about skeleton in spite of change of the colour and illumination. This is the novelty presented by the authors of this study. It is because “RGB methods of re-id based on appearance” tended to fail when people appeared in changed clothes or on extreme illumination (Karianakis et al., 2018).
  4. The results of the study showed that if the features associated with “RGB-based appearance” can be combined with the estimated depth features developing visual ambiguities regarding the features of appearance can be reduced while people appear in extreme illumination and similar clothes.
  5. The distance between two co-variants matrices have been studied and Riemannian manifold method has been used to measure the “Euclidean distance between the Eigen-depth features corresponded.”
  6. The research methods that would be used in the current study is the extraction of the basic feature, studying the depth voxel covariance through descriptor, Local rotation invariance of Eigen values, analysis of the Eigen depth features can be used as a data collection method. Apart from that, this study does not include human participants; therefore, secondary data would be collected for analysis (Haque, Alahi & Fei-Fei, 2016).

H1. It could be hypothesized that the features of the skeleton is a significant fact in creating invariant body shape.

H2. There is a significant relationship between illumination changes along with the change of colour.

Here, the statistical data analysis is dependent upon the TGB-based method used here. The role of Eigen values have been measured through covariance matrices. C − 1 discriminant vectors would be extracted by LDA. It has been depicted in the study that when the depth features are not available a “kernelized implicit feature transfer scheme” can be used in order to estimate Eigendepth feature in implicit manner. It has also been found that the visual ambiguities can more be reduced with the help of the combined features of the RGB-based image and the estimated features of depth. It is considered to be true even in the cases of changed illumination and changed clothes that have been considered to be highly affecting the image of the body. It is may be due to the perceptual differences that the validation of the images differs from the kernelized implicit feature.

The research’s internal validation is high as the authors have studied the variables of the research in a controlled manner. It is likely for the grouping of estimated depth feature and RGB-based depth features to reduce the ambiguity. However, the research has low external validity as the other situation such as changes of temperature and presence of other variables has not been studied.

Research Methodology - Abstract C

  1. How the “global average pooling layer enables the convolutional neural network (CNN)” in explicit manners.
  2. The secondary research question that has been attempted to answer is if the “convolutional neural network (CNN)” possesses the ability of remarkable localization in spite of taking training on image-level labels.
  3. The current study has showed novelty in the research conduction by attempting to clear up if the technique applying it in variety of tasks through actually building basic localizable deep representation. This has not been featured in the existing other research bodies as per the study conducted by the authors (Perera & Patel, 2019).
  4. It has been depicted by the authors that global average pooling is apparently simple and in spite of this, “37.1% top-5 error” can be achieved for the localization of object on the “dataset ILSVRC 2014.” It has been demonstrated by the current researchers that the network presently in focus is proficient of localizing the regions of discriminative image in the context of variety of tasks in spite of not gaining training support.
  5. In order to answer the current research question, the authors have evaluated the impacts of using CAM in the different CNNs such as VGGnet, AlexNet, and GoogLeNet. In the context of each of these networks fully-connected layers have been removed before gaining the final output and replaced by the GAP followed by another layer of softmax that are fully-connected. This is because; it has been found by the authors that removal of the fully connected layers is able to reduce the network parameters. It also results in some performance drop in the classification.
  6. Here the secondary data collection method would be used as this provides highly coherent and rich information regarding the study variables that cannot be obtained through classification. Both the classification performance and localisation performance would be studied in the research (Ouyang et al. 2017).

H1- The removal of fully connected layers is responsible for increasing the parameters of Network.

The conclusion in the abstract includes that the developed network is responsible for localising the discriminative regions of image. It has been further concluded in the study that “Class Activation Mapping (CAM) for CNNs with global average pooling” can enable CNNs that are classification trained CNNs. The class scores are visualized and predicted on any image through discriminating the objective parts that CNNs detect.

The external validity of the current study is low as the study has been conducted within a controlled situation where the variables are manipulated by the researchers. However, its internal validity is high as arrangement of the variables in the same manner may provide similar results in the similar setting.

Reference List for Research Methodology

Gauld, C. S., Lewis, I., White, K. M., Fleiter, J. J., & Watson, B. (2017). “Smartphone use while driving: what factors predict young drivers' intentions to initiate, read, and respond to social interactive technology?” Published in Computers in Human Behavior, Vol. 76, Issue 1, p. 174-183,(2017).

Bulling, A., & Kunze, K. (2016). Eyewear computers for human-computer interaction. interactions23(3), 70-73.

Karianakis, N., Liu, Z., Chen, Y., & Soatto, S. (2018). Reinforced temporal attention and split-rate transfer for depth-based person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 715-733).

Haque, A., Alahi, A., & Fei-Fei, L. (2016). Recurrent attention models for depth-based person identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1229-1238).

Perera, P., & Patel, V. M. (2019). Learning deep features for one-class classification. IEEE Transactions on Image Processing28(11), 5450-5463.

Ouyang, W., Zhou, H., Li, H., Li, Q., Yan, J., & Wang, X. (2017). Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE transactions on pattern analysis and machine intelligence40(8), 1874-1887.

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