Abstract

Abstract
Searching is a crucial time-consuming part of many programs, and using a good search method instead of a bad one often leads to a substantial increase in performance. Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and concurrent hash map design that fully supports the concurrent search, insert and remove operations on hash tries designed to store sorted keys. To the best of our knowledge, our design is the first concurrent hash map design that puts together the following characteristics: (i) use fixed size data structures; (ii) use persistent memory references; (iii) be lock-free; and (iv) store sorted keys. Experimental results show that our design is quite competitive when compared against other state-of-the-art designs implemented in Java.

Abstract
This paper presents LRMalloc, a lock-free memory allocator that leverages lessons of modern memory allocators and combines them with a lock-free scheme. Current state-of-the-art memory allocators possess good performance but lack desirable lock-free properties, such as, priority inversion tolerance, kill-tolerance availability, and/or deadlock and livelock immunity. LRMalloc’s purpose is to show the feasibility of lock-free memory management algorithms, without sacrificing competitiveness in comparison to commonly used state-of-the-art memory allocators, especially for concurrent multithreaded applications. © 2019, Springer Nature Switzerland AG.

Abstract

Abstract
Current challenges in machine learning include dealing with temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. This dynamic nature tends to limit the performance of traditional static learning models and dynamic learning strategies must be put forward. However, acquiring the performance of those strategies is not a straightforward issue, as sample's dependency undermines the use of validation techniques, like crossvalidation. In this paper we propose to use the McNemar's test to compare two distinct approaches that tackle adaptive learning in dynamic environments, namely DARK (Drift Adaptive Retain Knowledge) and Learn++. NSE (Learn++ for Non-Stationary Environments). The validation is based on a Twitter case study benchmark constructed using the DOTS (Drift Oriented Tool System) dataset generator. The results obtained demonstrate the usefulness and adequacy of using McNemar's statistical test in dynamic environments where time is crucial for the learning algorithm. © 2018 IEEE.

Abstract
Information Extraction (IE) and fusion are complex fields and have been useful in several domains to deal with heterogeneous data sources. Criminal police are challenged in forensics activities with the extraction, processing and interpretation of numerous documents from different types and with distinct formats (templates), such as narrative criminal reports, police databases and the result of OSINT activities, just to mention a few. Such challenges suggest, among others, to cope with and manually connect some hard to interpret meanings, such as license plates, addresses, names, slang and figures of speech. This paper aims to deal with forensic IE and fusion, thus a system was proposed to automatically extract, transform, clean, load and connect police reports that arrived from different sources. The same system aims to help police investigators to identify and correlate interesting extracted entities. © 2020, Springer Nature Switzerland AG.