Two noble lives. Samuel Gridley Howe, Julia Ward Howe,

Mode of access: Internet.

Julia Jackson Christian,

Mode of access: Internet.

Julia Ward Howe, 1819-1910,

One leaf of Mrs. Howe's manuscript mounted before frontispiece of vol. I.

Guntheri Christophori Schelhammeri med. doct. & ejusdem facultatis in Academia Julia professor. p. nec non naturæ curiosor. collegæ De auditu liber unus : quo plerorumque omnium doctorum sententiæ examinantur, & auditis ratio nova methodo, ex ipsus naturæ legibus, explicatur.

Last group of four pages is errata.

Julia france and her times

Mode of access: Internet.

Espaço de todos ou de ninguém: análise morfológica de transformações espaciais dos Conjuntos Residenciais Parque Serrambi em Natal/ RN

This work analyses the effects of form over the emergence of new patterns of use and occupation in public spaces of housing estates designed in the 1980s and 1990s in Natal’s municipality (Brazil). We start from the premise that form acts on social process (HILLIER; HANSON, 1984), and verify how much the original spatial configuration of Parque Serrambi housing states contributed to the creation of new spatial patterns following the interventions of the people living there. The Serrambi states were built in Natal’s south based on an urbanization model following modernist’s principals and aimed to supply demands for popular dwellings. They were one of the last estates financed by the former National Dwelling Bank (BNH) and supervised by the Dwelling Cooperatives Orientation Institute (INOCOOP), materialized a spatial form different from the highrise dwelling experiences from the same period. The results were obtained through configurational analysis based on Space Syntax conceptual and methodological framework, in which space and society are viewed as interrelated. The analyses was based on represanting and quantifying spatial properties and identificatying social patterns related to the interventions. We identified that the original spatial configurations, associated to subtle changes in the states social patterns, analysed independently of non-morphological categories, gave way to the occupation patterns verrified in both Serrambi cases.

Construção de um classificador automático de severidade de bugs para sistemas open source

Software bug analysis is one of the most important activities in Software Quality. The rapid and correct implementation of the necessary repair influence both developers, who must leave the fully functioning software, and users, who need to perform their daily tasks. In this context, if there is an incorrect classification of bugs, there may be unwanted situations. One of the main factors to be assigned bugs in the act of its initial report is severity, which lives up to the urgency of correcting that problem. In this scenario, we identified in datasets with data extracted from five open source systems (Apache, Eclipse, Kernel, Mozilla and Open Office), that there is an irregular distribution of bugs with respect to existing severities, which is an early sign of misclassification. In the dataset analyzed, exists a rate of about 85% bugs being ranked with normal severity. Therefore, this classification rate can have a negative influence on software development context, where the misclassified bug can be allocated to a developer with little experience to solve it and thus the correction of the same may take longer, or even generate a incorrect implementation. Several studies in the literature have disregarded the normal bugs, working only with the portion of bugs considered severe or not severe initially. This work aimed to investigate this portion of the data, with the purpose of identifying whether the normal severity reflects the real impact and urgency, to investigate if there are bugs (initially classified as normal) that could be classified with other severity, and to assess if there are impacts for developers in this context. For this, an automatic classifier was developed, which was based on three algorithms (Näive Bayes, Max Ent and Winnow) to assess if normal severity is correct for the bugs categorized initially with this severity. The algorithms presented accuracy of about 80%, and showed that between 21% and 36% of the bugs should have been classified differently (depending on the algorithm), which represents somewhere between 70,000 and 130,000 bugs of the dataset.

Lt. Julia Tucker of the NYPD Rape Squad

General note: Title and date provided by Bettye Lane.