Our lives are heavily influenced by persuasive communication, and it is essential in almost any types of social interactions from business negotiation to conversation with our friends and family. With the rapid growth of social multimedia websites, it is becoming ever more important and useful to understand persuasiveness in the context of social multimedia content online. In this paper, we introduce our newly created multimedia corpus of 1,000 movie review videos obtained from a social multimedia website called ExpoTV.com, which will be made freely available to the research community. Our research results presented here revolve around the following 3 main research hypotheses. Firstly, we show that computational descriptors derived from verbal and nonverbal behavior can be predictive of persuasiveness. We further show that combining descriptors from multiple communication modalities (audio, text and visual) improve the prediction performance compared to using those from single modality alone. Secondly, we investigate if having prior knowledge of a speaker expressing a positive or negative opinion helps better predict the speaker's persuasiveness. Lastly, we show that it is possible to make comparable prediction of persuasiveness by only looking at thin slices (shorter time windows) of a speaker's behavior.
In the many-core era, scalable coherence and on-chip interconnects are crucial for shared memory processors. While snoopy coherence is common in small multicore systems, directory-based coherence is the de facto choice for scalability to many cores, as snoopy relies on ordered interconnects which do not scale. However, directory-based coherence does not scale beyond tens of cores due to excessive directory area overhead or inaccurate sharer tracking. Prior techniques supporting ordering on arbitrary unordered networks are impractical for full multicore chip designs.We present SCORPIO, an ordered mesh Network-on-Chip (NoC) architecture with a separate fixed-latency, bufferless network to achieve distributed global ordering. Message delivery is decoupled from the ordering, allowing messages to arrive in any order and at any time, and still be correctly ordered. The architecture is designed to plug-and-play with existing multicore IP and with practicality, timing, area, and power as top concerns. Full-system 36 and 64-core simulations on SPLASH-2 and PARSEC benchmarks show an average application runtime reduction of 24.1% and 12.9%, in comparison to distributed directory and AMD HyperTransport coherence protocols, respectively.The SCORPIO architecture is incorporated in an 11 mm-by-13 mm chip prototype, fabricated in IBM 45nm SOI technology, comprising 36 Freescale e200 Power Architecture TM cores with private L1 and L2 caches interfacing with the NoC via ARM AMBA, along with two Cadence on-chip DDR2 controllers. The chip prototype achieves a post synthesis operating frequency of 1 GHz (833 MHz post-layout) with an estimated power of 28.8 W (768 mW per tile), while the network consumes only 10% of tile area and 19 % of tile power.
GPT-3 shows remarkable in-context learning ability of large-scale language models (LMs) trained on hundreds of billion scale data. Here we address some remaining issues less reported by the GPT-3 paper, such as a non-English LM, the performances of different sized models, and the effect of recently introduced prompt optimization on in-context learning. To achieve this, we introduce Hy-perCLOVA, a Korean variant of 82B GPT-3 trained on a Korean-centric corpus of 560B tokens. Enhanced by our Korean-specific tokenization, HyperCLOVA with our training configuration shows state-of-the-art in-context zero-shot and few-shot learning performances on various downstream tasks in Korean. Also, we show the performance benefits of promptbased learning and demonstrate how it can be integrated into the prompt engineering pipeline. Then we discuss the possibility of materializing the No Code AI paradigm by providing AI prototyping capabilities to nonexperts of ML by introducing HyperCLOVA studio, an interactive prompt engineering interface. Lastly, we demonstrate the potential of our methods with three successful in-house applications.
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