Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem has only been addressed via node clustering methods. In this paper, we extend the popular Adjusted Classify & Count (ACC) method to graphs. We show that the prior probability shift assumption upon which ACC relies is often not fulfilled and propose two novel graph quantification techniques: Structural importance sampling (SIS) makes ACC applicable in graph domains with covariate shift. Neighborhood-aware ACC improves quantification in the presence of non-homophilic edges. We show the effectiveness of our techniques on multiple graph quantification tasks.

Quantification represents the problem of predicting class distributions in a given target set. It also represents a growing research field in supervised machine learning, for which a large variety of different algorithms has been proposed in recent years. However, a comprehensive empirical comparison of quantification methods that supports algorithm selection is not available yet. In this work, we close this research gap by conducting a thorough empirical performance comparison of 24 different quantification methods. To consider a broad range of different scenarios for binary as well as multiclass quantification settings, we carried out almost 3 million experimental runs on 40 data sets. We observe that no single algorithm generally outperforms all competitors, but identify a group of methods including the Median Sweep and the DyS framework that perform significantly better in binary settings. For the multiclass setting, we observe that a different, broad group of algorithms yields good performance, including the Generalized Probabilistic Adjusted Count, the readme method, the energy distance minimization method, the EM algorithm for quantification, and Friedman's method. More generally, we find that the performance on multiclass quantification is inferior to the results obtained in the binary setting. Our results can guide practitioners who intend to apply quantification algorithms and help researchers to identify opportunities for future research.

The PlayStation Vita is hanging on by a thread. Sony Worldwide Studios president Shuhei Yoshida said in 2015 there was no hope for a follow-up to the handheld console, and since then, the Vita machine has been quietly winding down. Sony is halting production of physical Vita games across Europe and North America on March 31st, 2019, though Sony Japan will continue to churn out carts in the domestic market -- for now. The Vita has garnered a cult following since its Japanese launch in 2011 (2012 for Western audiences). It entered the scene pitched as a powerful handheld with a digital marketplace, OLED display and a broad variety of touch- and movement-based input methods. Major franchises flooded the device, including Persona, Call of Duty, Assassin's Creed and LittleBigPlanet, and it became a welcome home for indie games like Hotline Miami, Murasaki Baby, OlliOlli and Fez.

This paper reports a cradle sound system creating and reproducing sounds and music appropriate for human sleep with heartbeat and respiration signals sensed by biological sensors. To get further supporting evidence, we started a study aiming at exploring what sound attributes, such as waveforms, tones, and tempos, are necessary for a sound capable of improving sleep latency. We expected that a cradle sound whose tempo was slightly slower than those of heartbeat and respiration could slow them and could promote natural sleep. Subjects listening to this sound during their sleep showed: (1) Multiple sound types with different tones have an effect to shorten sleep latency. (2) Remarkable effects are observed in subjects with long sleep latency. (3) Sustained synthetic chord used for inducing respiration did not improve sleep latency. (4) There is no correlation between subject’s sensibility evaluation to sound and the effect shortening sleep latency.

This paper aims at developing the ambient intelligence sleep system that can derive a good sleep by providing a personally adapted sound. For this purpose, this paper explores the sounds that have a potential of deriving a good sleep and investigates their effect from the several viewpoints (e.g., the sleep latent time). To promote a good sleep, this paper focuses on heartbeat and respiration which are related to a sleep (i.e., its rate decreases as falling asleep) and proposes the ambient intelligent sleep system that provides the sound adjusted to the heartbeat and/or respiration rates, which are automatically measured by the piezoelectric-based mattress sensor without connecting any devices to human’s body. The human subjective experiments of the six subjects for a nap case and the seven subjects for a night sleep case have revealed the following implications: (1) the new wave sound adjusted to both the heartbeat rate (x 1.05) and respiration rate (x 1.05) can shorten the sleep latent time in a nap case in comparison with no sound or the other four types of the sounds; (2) the combination of the two sound sources (adjusted by the heartbeat and respiration rates) contributes to shortening the sleep latent time in comparison with one sound source; (3) the new wave sound can shorten not only the sleep latent time but also the Non-REM3 latent time in a night sleep case in comparison with no sound; and (4) the new wave sound can keep not only an appropriate sleep cycle but also the very similar sleep cycle from the Non-REM to the next one in a night sleep case in comparison with no sound.