Map++: A Crowd-sensing System for Automatic Map Semantics Identification

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.Comment: Published in the Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (IEEE SECON 2014

Developing a Formal Model for Mind Maps

Mind map is a graphical technique, which is used to represent words, concepts, tasks or other connected items or arranged around central topic or idea. Mind maps are widely used, therefore exist plenty of software programs to create or edit them, while there is none format for the model representation, neither a standard format. This paper presents and effort to propose a formal mind map model aiming to describe the structure, content, semantics and social connections. The structure describes the basic mind map graph consisted of a node set, an edge set, a cloud set and a graphical connections set. The content includes the set of the texts and objects linked to the nodes. The social connections are the mind maps of other users, which form the neighborhood of the mind map owner in a social networking system. Finally, the mind map semantics is any true logic connection between mind map textual parts and a concept. Each of these elements of the model is formally described building the suggested mind map model. Its establishment will support the application of algorithms and methods towards their information extraction

Lexical typology through similarity semantics: Toward a semantic map of motion verbs

This paper discusses a multidimensional probabilistic semantic map of lexical motion verb stems based on data collected from parallel texts (viz. translations of the Gospel according to Mark) for 100 languages from all continents. The crosslinguistic diversity of lexical semantics in motion verbs is illustrated in detail for the domain of `go', `come', and `arrive' type contexts. It is argued that the theoretical bases underlying probabilistic semantic maps from exemplar data are the isomorphism hypothesis (given any two meanings and their corresponding forms in any particular language, more similar meanings are more likely to be expressed by the same form in any language), similarity semantics (similarity is more basic than identity), and exemplar semantics (exemplar meaning is more fundamental than abstract concepts)

Semantics out of context: nominal absolute denotations for first-order logic and computation

Call a semantics for a language with variables absolute when variables map to fixed entities in the denotation. That is, a semantics is absolute when the denotation of a variable a is a copy of itself in the denotation. We give a trio of lattice-based, sets-based, and algebraic absolute semantics to first-order logic. Possibly open predicates are directly interpreted as lattice elements / sets / algebra elements, subject to suitable interpretations of the connectives and quantifiers. In particular, universal quantification "forall a.phi" is interpreted using a new notion of "fresh-finite" limit and using a novel dual to substitution. The interest of this semantics is partly in the non-trivial and beautiful technical details, which also offer certain advantages over existing semantics---but also the fact that such semantics exist at all suggests a new way of looking at variables and the foundations of logic and computation, which may be well-suited to the demands of modern computer science

Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been release