Bridging the gap between neurons and cognition through assemblies of neurons

During recent decades, our understanding of the brain has advanced dramatically at both the cellular and molecular levels and at the cognitive neurofunctional level; however, a huge gap remains between the microlevel of physiology and the macrolevel of cognition. We propose that computational models based on assemblies of neurons can serve as a blueprint for bridging these two scales. We discuss recently developed computational models of assemblies that have been demonstrated to mediate higher cognitive functions such as the processing of simple sentences, to be realistically realizable by neural activity, and to possess general computational power

Ubiquitous Social Networks: Opportunities and Challenges for Privacy-Aware User Modelling

Privacy has been recognized as an important topic in the Internet for a long time, and technological developments in the area of privacy tools are ongoing. However, their focus was mainly on the individual. With the proliferation of social network sites, it has become more evident that the problem of privacy is not bounded by the perimeters of individuals but also by the privacy needs of their social networks. The objective of this paper is to contribute to the discussion about privacy in social network sites, a topic which we consider to be severely under-researched. We propose a framework for analyzing privacy requirements and for analyzing privacy-related data. We outline a combination of requirements analysis, conflict-resolution techniques, and a P3P extension that can contribute to privacy within such sites.World Wide Web, privacy, social network analysis, requirements analysis, privacy negotiation, ubiquity, P3P

Parametric Hidden Markov Models for Recognition and Synthesis of Movements

In humanoid robotics, the recognition and synthesis of parametric movements plays an extraordinary role for robot human interaction. Such a parametric movement is a movement of a particular type (semantic), for example, similar pointing movements performed at different table-top positions. For understanding the whole meaning of a movement of a human, the recognition of its type, likewise its parameterization are important. Only both together convey the whole meaning. Vice versa, for mimicry, the synthesis of movements for the motor control of a robot needs to be parameterized, e.g., by the relative position a grasping action is performed at. For both cases, synthesis and recognition, only parametric approaches are meaningful as it is not feasible to store, or acquire all possible trajectories. In this paper, we use hidden Markov models (HMMs) extended in an exemplar-based parametric way (PHMM) to represent parametric movements. As HMMs are generative, they are well suited for synthesis as well as for recognition. Synthesis and recognition are carried out through interpolation of exemplar movements to generalize over the parameterization of a movement class. In the evaluation of the approach we concentrate on a systematical validation for two parametric movements, grasping and pointing. Even though the movements are very similar in appearance our approach is able to distinguish the two movement types reasonable well. In further experiments, we show the applicability for online recognition based on very noisy 3D tracking data. The use of a parametric representation of movements is shown in a robot demo, where a robot removes objects from a table as demonstrated by an advisor. The synthesis for motor control is performed for arbitrary table-top positions

Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions

Translating verbose information needs into crisp search queries is a phenomenon that is ubiquitous but hardly understood. Insights into this process could be valuable in several applications, including synthesizing large privacy-friendly query logs from public Web sources which are readily available to the academic research community. In this work, we take a step towards understanding query formulation by tapping into the rich potential of community question answering (CQA) forums. Specifically, we sample natural language (NL) questions spanning diverse themes from the Stack Exchange platform, and conduct a large-scale conversion experiment where crowdworkers submit search queries they would use when looking for equivalent information. We provide a careful analysis of this data, accounting for possible sources of bias during conversion, along with insights into user-specific linguistic patterns and search behaviors. We release a dataset of 7,000 question-query pairs from this study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape

Word processing in languages using non-alphabetic scripts: The cases of Japanese and Chinese

This thesis investigates the processing of words written in Japanese kanji and Chinese hànzì, i.e. logographic scripts. Special attention is given to the fact that the majority of Japanese kanji have multiple pronunciations (generally depending on the combination a kanji forms with other characters). First, using masked priming, it is established that upon presentation of a Japanese kanji multiple pronunciations are activated. In subsequent experiments using word naming with context pictures it is concluded that both Chinese hànzì and Japanese kanji are read out loud via a direct route from orthography to phonology. However, only Japanese kanji become susceptible to semantic or phonological context effects as a result of a cost due to the processing of multiple pronunciations. Finally, zooming in on the size of the articulatory planning unit in Japanese it is concluded that the mora as a phonological unit best complies with the observed data pattern and not the phoneme or the syllabl