Decentralized Probabilistic World Modeling with Cooperative Sensing

Drawing on the projected increase in computing power, solid-state storage and network communication capacity to be available on personal mobile devices, we propose to build and maintain without prior knowledge a fully distributed decentralized large-scale model of the physical world around us using probabilistic methods. We envisage that, by using the multimodal sensing capabilities of modern personal devices, such a probabilistic world model can be constructed as a collaborative effort of a community of participants, where the model data is redundantly stored on individual devices and updated and refined through short-range wireless peer-to-peer communication. Every device holds model data describing its current surroundings, and obtains model data from others when moving into unknown territory. The model represents common spatio-temporal patterns as observed by multiple participants, so that rogue participants can not easily insert false data and only patterns of general applicability dominate. This paper aims to describe – at a conceptual level – an approach for building such a distributed world model. As one possible world modeling approach, it discusses compositional hierarchies, to fuse the data from multiple sensors available on mobile devices in a bottom-up way. Furthermore, it focuses on the intertwining between building a decentralized cooperative world model and the opportunistic communication between participants

Multilevel Modelling with Spatial Effects

In multilevel modelling, interest in modeling the nested structure of hierarchical data has been accompanied by increasing attention to di¤erent forms of spatial interactions across different levels of the hierarchy. Neglecting such interactions is likely to create problems of inference, which typically assumes independence. In this paper we review approaches to multilevel modelling with spatial e¤ects, and attempt to connect the two literatures, discussing the advantages and limitations of various approaches.Multilevel Modelling, Spatial E¤ects, Fixed E¤ects, Random E¤ects, IGLS, FGS2SLS.

Developing Integrated Waste Management Systems: Information Needs and the Role of Locally Based Data

No abstract available

Team to Market (T2M): Creating High Performance Teams in the Digital Age

1. Teams are the essential means of product or service delivery and the fundamental building blocks of modern organisations. An effective team can produce results far outperforming a collection of even the most talented individuals when team members coalesce and jell into a single, well-functioning, fully-aligned organism. This report advances the notion of “Team to Market” (T2M) to help business leaders and knowledge workers understand, create and lead high performance teams in the digital age

Cognitive satellite communications and representation learning for streaming and complex graphs.

This dissertation includes two topics. The first topic studies a promising dynamic spectrum access algorithm (DSA) that improves the throughput of satellite communication (SATCOM) under the uncertainty. The other topic investigates distributed representation learning for streaming and complex networks. DSA allows a secondary user to access the spectrum that are not occupied by primary users. However, uncertainty in SATCOM causes more spectrum sensing errors. In this dissertation, the uncertainty has been addressed by formulating a DSA decision-making process as a Partially Observable Markov Decision Process (POMDP) model to optimally determine which channels to sense and access. Large-scale networks have attracted many attentions to discover the hidden information from big data. Particularly, representation learning embeds the network into a lower vector space while maximally preserving the similarity among nodes. I propose a real-time distributed graph embedding algorithm (RTDGE) which is capable of distributively embedding the streaming graph by combining a novel edge partition approach and an incremental negative sample approach. Furthermore, a platform is prototyped based on Kafka and Storm. Real-time Twitter network data can be retrieved, partitioned and processed for state-of-art tasks. For knowledge graphs, existing works cannot capture the complex connection patterns and never consider the impacts from complicated relations, due to the unquantifiable relationships. A novel embedding algorithm is proposed to hierarchically measure the structural similarity and the impacts from relations by constructing a multi-layer graph. Then, an advanced representation learning model is designed based on an entity\u27s context generated by random walks on the multi-layer content graph

Making the most of clade selection

Clade selection is unpopular with philosophers who otherwise accept multilevel selection theory. Clades cannot reproduce, and reproduction is widely thought necessary for evolution by natural selection, especially of complex adaptations. Using microbial evolutionary processes as heuristics, I argue contrariwise, that (1) clade growth (proliferation of contained species) substitutes for clade reproduction in the evolution of complex adaptation, (2) clade-level properties favoring persistence – species richness, dispersal, divergence, and possibly intraclade cooperation – are not collapsible into species-level traits, (3) such properties can be maintained by selection on clades, and (4) clade selection extends the explanatory power of the theory of evolution

Dwelling on ontology - semantic reasoning over topographic maps

The thesis builds upon the hypothesis that the spatial arrangement of topographic features, such as buildings, roads and other land cover parcels, indicates how land is used. The aim is to make this kind of high-level semantic information explicit within topographic data. There is an increasing need to share and use data for a wider range of purposes, and to make data more definitive, intelligent and accessible. Unfortunately, we still encounter a gap between low-level data representations and high-level concepts that typify human qualitative spatial reasoning. The thesis adopts an ontological approach to bridge this gap and to derive functional information by using standard reasoning mechanisms offered by logic-based knowledge representation formalisms. It formulates a framework for the processes involved in interpreting land use information from topographic maps. Land use is a high-level abstract concept, but it is also an observable fact intimately tied to geography. By decomposing this relationship, the thesis correlates a one-to-one mapping between high-level conceptualisations established from human knowledge and real world entities represented in the data. Based on a middle-out approach, it develops a conceptual model that incrementally links different levels of detail, and thereby derives coarser, more meaningful descriptions from more detailed ones. The thesis verifies its proposed ideas by implementing an ontology describing the land use ‘residential area’ in the ontology editor Protégé. By asserting knowledge about high-level concepts such as types of dwellings, urban blocks and residential districts as well as individuals that link directly to topographic features stored in the database, the reasoner successfully infers instances of the defined classes. Despite current technological limitations, ontologies are a promising way forward in the manner we handle and integrate geographic data, especially with respect to how humans conceptualise geographic space