Slam Algorithms for Different Map Representations

The paper discusses the generalized simultaneous localization and mapping problem statement from the standpoint of the Bayesian approach and its relationship with algorithms for different map representations. The two-dimensional example describes the linearized simultaneous localization and mapping algorithm for the mobile platform in two-dimensional space.

Phonological redeployment and the mapping problem: Cross-linguistic E-similarity is the beginning of the story, not the end

In this research note I want to address some misunderstandings about the construct of redeployment and suggest that we need to fit these behavioural data from Yang, Chen and Xiao (YCX) into a broader context. I will suggest that these authors’ work is not just about the failure of three models to predict equivalence classification. Equivalence classification is not the end of the story but only the beginning. We need to look at what cues are detected in the input, which subset of the input becomes intake, and how this intake is parsed onto phonological structures. The empirical results of YCX should not be viewed as some sort of non-result inasmuch as none of the proposed predictors of Mandarin equivalence classification foresaw that the Russian prevoiced stops and short-lag stops would be equated with the Mandarin short-lag stops. Rather, the empirical results need to be contextualized by considering such factors as cue reweighting as part of the learning theory which maps intake onto phonological representations. In this light, the results are not a repudiation of phonological redeployment, but help to shed light on the parsing of the acoustic signal, the importance of robust burst-release cues, and the non-local nature of L2 phonological learning (as opposed to noticing).

Analysis and object-oriented generalization of meteorological data in solving the bioclimatic mapping problem

The focus of the paper is interrelation and character of changes in climate factors in Taimyr and the North of Evenki in the summer-autumn period between 1970 and 2020. Bioclimatic fields of the reindeer range as a form of object-oriented generalization of meteorological data have been constructed for the “average” and the most contrasting years in terms of temperature indices.

Character Design for Educational Games “Caring for The Urban Forest in Jakarta”

The existence of the urban forest in Jakarta become a valuable asset for its residents. Unfortunately, the urban forest less aware of its existence. This unconsciousness due to lack of information about the existence of the urban forest itself. The design aims to create unique and attractive character, which helps inform the existence of the urban forest in Jakarta, through educational games. The design method through the stages of observation in the form of visual data search, analysis elaboration mapping problem, then visually determining the essential character as the main idea. The result is a character design with the function as the "spokes person" that allows users to know the existence of the urban forest in Jakarta

Mapping problem using text mining to boost tourism industry: is it possible?

TripAdvisor has become a credential traveling platform for tourists worldwide to set travel plans. The widespread of big data in online platforms urges the use of text mining to benefit some sectors, including in the tourism industry. This study aimed to investigate the information extraction based on the online reviews on TripAdvisor for Gili Trawangan tourist destinations. The method used in this research was text mining with Support Vector Machine (SVM) to classify the online reviews that categorized into two classes, positive class and negative class. The results of information extraction show that the issue of horse cruelty, bad waste management, and ecosystem vulnerability dominated the negative sentiments. These negative sentiments need to be handled professionally by the tourism enterprise to boost the tourism industry in Gili Trawangan.

Mass-constrained basin basement mapping

The irregular interface model setting side by side two dense homogeneous media has found many applications in gravity-data exploration such as for petroleum and gas in sedimentary basins, groundwater resources in buried paleochannels, characterization of abandoned landfills, and variable regolith-depth mapping. Despite its simplicity and wide range of applicability, the determination of the interface position from inverting surface gravity data configures an ill-posed problem requiring specialized regularizing procedures to produce reliable results. Common approaches to obtain stable and reliable solutions require a judicious choice of regularizing functionals, each of them able to convey a desired geologic attribute that the unknown interface is expected to feature. In assuming a style that the unknown interface may have, a mathematical procedure is elected to convey such an attribute when the interface is mapped from gravity data inversion. We have developed a different approach to the interface mapping problem by imposing a common constraint that all model solutions must have, meanwhile preventing oscillations for the interface to be mapped. As a constraint that the solutions must have, we fix the volume or the cross section for 2D structures that the anomalous density structure has. This volume (or 2D cross section) is determined by applying the mass excess theorem to the measured gravity data and assuming as known the density contrast caused by the two media paired by the interface. We find that this simple formulation for the interface-mapping problem is effective in imaging a variety of basin styles without introducing specific information about the interface attributes. Our technique is applied to invert previously published gravity data in cases with good drillhole control or with a known interface.

Applying Genetic Algorithm to Solve Partitioning and Mapping Problem for Mesh Network-on-Chip Systems

This paper presents a genetic based approach to the partitioning and mapping of multicore SoC cores over a NoC system that uses mesh topology. The proposed algorithm performs the partitioning and mapping by reducing communication cost and minimizing power consumption by placing those intercommunicated cores as close as possible together. A program developed in C++ in which the provided specification of the multicore MPSoC system captures all data dependencies before any start of the design process. Experimental results of several multimedia benchmarks demonstrates that the genetic-based approach able to find different satisfied implementations to the problem of partitioning and mapping of MPSoC cores over mesh-based NoC system that satisfies design goals.

DBTagger

Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using adhoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called DBTagger (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, 92.4% on the average. Our results also indicate that DBTagger is faster than its counterparts up to 10000 times and scalable for bigger databases.