Conclusion

This concluding chapter uses British free-to-air television broadcasting network Channel 4’s “Superhumans Return” advertising campaign for its coverage of the 2016 Paralympic Games as a case study with which to explore the overlaps between nineteenth-century and contemporary cultural representations of prosthesis users. It highlights the way that contemporary sources, including Channel 4’s campaign, interrogate a privileging of normalcy while remaining encoded by ableist inclinations. The chapter draws together the various strands of the book’s argument to make the case that the literary history of prosthesis is rich, complicated, and conflicted.

Rewitched: Retextuality and the Queering of Bewitched

At the same time the 1960s sitcom Bewitched aired in reruns next to drag queens on LOGOtv, a cable channel targeted to LGBTQ viewers, it also aired on the former National Christian Network channel (FamilyNet) immediately preceding a lineup of church programs featuring far-right, anti-gay hosts. Bewitched's ability to appeal to these very different channels’ brands and audiences underscores a textual vigor and sustainability for success in syndication that even the best so-called quality shows today lack. While some may deride a study of syndication (and reruns especially) as irrelevant and passé, syndicated programs are neither of those things if their continued popularity assures our familiarity with them. As a text, Bewitched is already supple enough to motivate two politically opposing media brands to pick it up, but the context of each of these channels’ flow, including commercials, station IDs, and edits to content, can make the experience of watching the same episode of any show on different channels a wholly different textual experience. This article returns to foundational theories of TV flow and intertextuality to propose retextuality as a theoretical and methodological intervention in studies of television. It argues that in syndication, the production labor of syndicators, executives, programmers, and marketing departments effectively retextualizes shows like Bewitched, offering scholars opportunities for new textual analyses and new insight into the marginalized and queer audiences syndicated programming often serves.

CONSUMING CONCERNS: LANGUAGE STYLE OF HUMOUR ON FOUR FEMALE STAND-UP COMEDIANS

Comedy is a male-dominated world as sociolinguistics theory also mentions that women employ less humour compared to men. However, female comedians also appeared on television. In Indonesia, Stand-Up Comedy Indonesia (SUCI) is a popular stand-up comedy competition broadcasted by a reputable national network channel, Kompas TV. Interestingly, numerous young female stand-up comedians also took part as participants of SUCI. This study aimed to analyse humour performed by four female comedian finalists from different cultural backgrounds such as Arab-Indonesian, Chinese-Indonesian, British expatriate, and Indonesian with Batak ethnicity. This study employed a qualitative method by applying various techniques such as video selection, downloading, transcribing relevant data, reducing data, applying theories, and concluding. The data were analysed using the theory of women's language and the theory of types and functions of humour. The study found that women employed humour in stand-up comedy to speak up their minds and share their concerns with the audiences using various language styles, including rhetorical questions, hypercorrect grammars, taboo words, and rising intonations. The humour was employed primarily to share their concerns toward sensitive cultural issues such as stereotypes and identity, sexism, relationship and gender equality, and body positivity. In addition, it contributes to enriching the theory of women's language style and humour. Keywords: humour, stand up comedy competition, women's language style, identity

CSA6D: Channel-Spatial Attention Networks for 6D Object Pose Estimation

Abstract6D object pose estimation plays a crucial role in robotic manipulation and grasping tasks. The aim to estimate the 6D object pose from RGB or RGB-D images is to detect objects and estimate their orientations and translations relative to the given canonical models. RGB-D cameras provide two sensory modalities: RGB and depth images, which could benefit the estimation accuracy. But the exploitation of two different modality sources remains a challenging issue. In this paper, inspired by recent works on attention networks that could focus on important regions and ignore unnecessary information, we propose a novel network: Channel-Spatial Attention Network (CSA6D) to estimate the 6D object pose from RGB-D camera. The proposed CSA6D includes a pre-trained 2D network to segment the interested objects from RGB image. Then it uses two separate networks to extract appearance and geometrical features from RGB and depth images for each segmented object. Two feature vectors for each pixel are stacked together as a fusion vector which is refined by an attention module to generate a aggregated feature vector. The attention module includes a channel attention block and a spatial attention block which can effectively leverage the concatenated embeddings into accurate 6D pose prediction on known objects. We evaluate proposed network on two benchmark datasets YCB-Video dataset and LineMod dataset and the results show it can outperform previous state-of-the-art methods under ADD and ADD-S metrics. Also, the attention map demonstrates our proposed network searches for the unique geometry information as the most likely features for pose estimation. From experiments, we conclude that the proposed network can accurately estimate the object pose by effectively leveraging multi-modality features.

StegoFrameOrder—MAC Layer Covert Network Channel for Wireless IEEE 802.11 Networks

The proposed StegoFrameOrder (SFO) method enables the transmission of covert data in wireless computer networks exploiting non-deterministic algorithms of medium access (such as the distributed coordination function), especially in IEEE 802.11 networks. Such a covert channel enables the possibility of leaking crucial information outside secured network in a manner that is difficult to detect. The SFO method embeds hidden bits of information in the relative order of frames transmitted by wireless terminals operating on the same radio channel. The paper presents an idea of this covert channel, its implementation, and possible variants. The paper also discusses implementing the SFO method in a real environment and the experiments performed in the real-world scenario.

Performance Analysis of Cascaded Hybrid Symmetric Encryption Models

Over a few years, there is rapid increase of exchange of data over the net has brought data confidentiality and its privacy to the fore front. Data confidentiality can be achieved by implementing cryptography algorithms during transmission of data which confirms that data remains secure and protected over an insecure network channel. In order to ensure data confidentiality and privacy, cryptography service encryption is used which makes data in unreadable form while the reverse process rearranges data in readable form and known as decryption. All encryption algorithms are intended to provide confidentiality to data, but their performance varies depending on many variables such as key size, type, number of rounds, complexity and data size used. In addition, although some encryption algorithms outperform others, they have been found to be prone to particular attacks. This paper reviews and summarizes the various common hybrid cascaded n-tier encryption models. Additionally, this paper compares and analyzes the performance of common hybrid cascaded 2-tier and 3-tier encryption models obtained during simulation based on encryption/decryption time, avalanche effect and throughput. The models compared with AES are 2-tier models (AES-TWOFISH, AES-BLOWFISH, TWOFISH-AES, BLOWFISH-AES, AES-SERPENT and SERPENT-TWOFISH) and 3-tier models (DES-BLOWFISH-AES, AES-TWOFISH-SERPENT and SERPENT-TWOFISH-AES). The hybrid cascaded model like AES-TWOFISH, AES-BLOWFISH and SERPENT-TWOFISH-AES are better hybrid models with respect to throughput and avalanche effect.

CNN Intrusion Detection for Threat Analysis of a Network

The technological advancement realized in the discovery and embrace of both IoT and IIoT is totally indispensable. Many systems and subsystems both robust and miniaturized have made their existence into the technical arena due to IoT. It goes without saying that IoT has brought into light very diverse benefits that cut across universal applications.However, the pre-requisite of a network channel existence for an IoT operation to be successful is the only pitfall that this essentially unique system possesses. There is a significant amount of danger associated with transmission networks. They have very substantial susceptibility to both online and offline threats by malicious cyber intentions.This paper focuses on the analyses of the threats posed to these IoT networks through Artificial Neural Networks. Specifically, a model is trained through recurrent and convolutional neural network to do intensive analysis on the threat intensity, type and threat source for data logging purposes. The Intruder detection system (IDS) explored in this paper registers a success rate of 99% based on the empirical data posed to the model.