The measurement of meaning

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

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Applied Missing Data Analysis

/pdf/multiple-factor-analysis-1xkff91jny.pdf

Multiple factor analysis.

This paper addresses the scope for more integrated general analysis of diversity in science, technology and society. It proposes a framework recognizing three necessary but individually insufficient properties of diversity. Based on 10 quality criteria, it suggests a general quantitative non-parametric diversity heuristic. This allows the systematic exploration of diversity under different perspectives, including divergent conceptions of relevant attributes and contrasting weightings on different diversity properties. It is shown how this heuristic may be used to explore different possible trade-offs between diversity and other aspects of interest, including portfolio interactions. The resulting approach offers a way to be more systematic and transparent in the treatment of scientific and technological diversity in a range of fields, including conservation management, research governance, energy policy and sustainable innovation.

A General Framework for Analysing Diversity in Science, Technology and Society

We propose reallocating the power resource among the code symbols in such a way to minimize the post decoding error probability of turbo code. We consider several power reallocation policies and investigate their performance in slowly varying Rayleigh flat fading channel. We show that the proposed scheme can reduce the post decoding error probability by two orders of magnitude and provide a power gain of 0.76 dB over the traditional equal power allocation among all code symbols. We also propose applying different power levels and cut-off thresholds on systematic and parity check bits depending on the channel gain and investigate the effect of channel gain estimation error.

Turbo code with power reallocation

We have designed and implemented an underwater modem using a general-purpose waterproof ultrasonic sensor which is approriate for UWSN owing to its low-power property and extremely low cost. As a succeeding work, we will try to improve the performance of the underwater modem in terms of data rate and communication distance by adopting state-of-art transmission technology.

Poster abstract: A point-to-multipoint acoustic modem for underwater wireless sensor networks

Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a \textit{proficiency-congruency dilemma} between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how users negotiate this dilemma. Using data from \textit{League of Legends}, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million users to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that player proficiency increases team performance more than team congruency. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly prioritize individuals' expertise and teams' compatibility.

The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35\%.

Analyzing Norm Violations in Live-Stream Chat

We present a model to predict fine-grained emotions along the continuous dimensions of valence, arousal, and dominance (VAD) with a corpus with categorical emotion annotations. Our model is trained by minimizing the EMD (Earth Mover’s Distance) loss between the predicted VAD score distribution and the categorical emotion distributions sorted along VAD, and it can simultaneously classify the emotion categories and predict the VAD scores for a given sentence. We use pre-trained RoBERTa-Large and fine-tune on three different corpora with categorical labels and evaluate on EmoBank corpus with VAD scores. We show that our approach reaches comparable performance to that of the state-of-the-art classifiers in categorical emotion classification and shows significant positive correlations with the ground truth VAD scores. Also, further training with supervision of VAD labels leads to improved performance especially when dataset is small. We also present examples of predictions of appropriate emotion words that are not part of the original annotations.

Sungjoon Park

Papers

Turbo code with power reallocation

Poster abstract: A point-to-multipoint acoustic modem for underwater wireless sensor networks

The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games

Analyzing Norm Violations in Live-Stream Chat

Dimensional Emotion Detection from Categorical Emotion