Double-Ventriloquism and Aegyo in Overwatch

This chapter considers how South Korean identity is represented in the online video game Overwatch through a close reading of the character D.Va. The term “double-ventriloquism” is proposed to explain the simultaneous representation of South Korean youth culture and an assumed North American player within D.Va’s vocal utterances. Through the combination of the audio generated by the game engine and the actions of the player, D.Va communicates messages of instruction, cheer, and defeat. Speaking through action and flavor dialogue, D.Va becomes recognizable as a sexy, cute, young, and techno-forward character whose character and persona the player audience connects with. However, they remain ignorant of the combined corporate and political interests of the creators. This chapter argues that, through “double ventriloquism,” D.Va manifests both North American game company Blizzard Entertainment’s (which owns Overwatch) financial interest and the South Korean state’s interest in exporting Korean cool to an American audience.”

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

<p>Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.</p>

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

<p>Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.</p>

Simultaneous Representation of Knowledge and Belief for Epistemic Planning with Belief Revision

We propose a novel approach to the problem of false belief revision in epistemic planning. Our state representations are pointed Kripke models with two binary relations over possible worlds: one representing agents' necessarily true knowledge, and one representing agents' possibly false beliefs. State transition functions maintain S5n properties in the knowledge relation and KD45n properties in the belief relation. When new information contradicts an agent's beliefs, belief revision draws new possible worlds from the agent's knowledge relation. Our method also improves upon prior work by accommodating false announcements. We develop our system as an extension to the mA* action language, presenting transition functions for ontic, sensing, and announcement actions.

Time Series Components Separation Based on Singular Spectral Analysis Visualization: an HJ-biplot Method Application

The extraction of essential features of any real-valued time series is crucial for exploring, modeling and producing, for example, forecasts. Taking advantage of the representation of a time series data by its trajectory matrix of Hankel constructed using Singular Spectrum Analysis, as well as of its decomposition through Principal Component Analysis via Partial Least Squares, we implement a graphical display employing the biplot methodology. A diversity of types of biplots can be constructed depending on the two matrices considered in the factorization of the trajectory matrix. In this work, we discuss the called HJ-biplot which yields a simultaneous representation of both rows and columns of the matrix with maximum quality. Interpretation of this type of biplot on Hankel related trajectory matrices is discussed from a real-world data set.

Pengujian Efek Resensi Dan Primasi Pada Informasi Audit Seri Panjang Dan Metode Mitigasinya Dengan Diskusi Kelompok

This study aims to test the order effect on the long series audit information and provide the form of mitigation by using group discussion so as to improve the quality of the decision. Order effects consist of reviews and primacy effects. A reviewer effect is a bias that occurs when an individual weighs the final received information larger for the overall information received. While the effect of primacy is a bias that occurs when individuals weigh greater initial information. Therefore, it is necessary to give a method so that the individual can consider the whole information for decision making which in this research use group discussion. This research uses 2x2x2 experimental design within subject with 81 participants from undergraduate accounting students.The results show that: (i) in the sequential ordering pattern for positive positive or negative positive sequence of positive information, the quality of individual decisions after group discussion is better than before the group discussion, (ii) the mitigation process does not occur in the simultaneous presentation pattern Positive positive or negative negative information sequence, (iii) sequential representation pattern more mitigated than simultaneous representation pattern.