The Prisma System: intelligent agents working on crime pattern analysis supported by geographic information systems

The process of extracting useful knowledge from large databases became one of the tasks of prior importance in today’s organizations. The collection of excessive amount of information makes very difficult its treatment and analysis without appropriated means. Police Departments are real examples of organizations that currently debate themselves with situations involving large volumes of distributed information and requiring effective real time decision making. Some of these situations are critical in the normal Police Department’s activities, namely the ones related to Crime Pattern Analysis. These are concerned with the recognition of spatial and temporal regularities in reported crime and the ability of predict future criminal activity. This is very important due the possibility to provide effective elements to increase patrol actions, improve priority investigations or even perform better public notification. Through the combination of Multi-Agent Systems and Geographic Information Systems technologies we design a computational system Intelligent Crime Pattern Analysis: the Prisma system. It considers a community of intelligent agents, divided essentially into two classes, that will be responsible respectively to populated specialized Data Marts and make Criminal Patterns Identification. With Prisma, Police Departments will be able to examine patterns related to notified incidents and analyze their movement in relation to police initiatives

Artificial Intelligence-enabled Automation for Compliance Checking against GDPR

Requirements engineering (RE) is concerned with eliciting legal requirements from applicable regulations to enable developing legally compliant software. Current software systems rely heavily on data, some of which can be confidential, personal, or sensitive. To address the growing concerns about data protection and privacy, the general data protection regulation (GDPR) has been introduced in the European Union (EU). Organizations, whether based in the EU or not, must comply with GDPR as long as they collect or process personal data of EU residents. Breaching GDPR can be charged with large fines reaching up to up to billions of euros. Privacy policies (PPs) and data processing agreements (DPAs) are documents regulated by GDPR to ensure, among other things, secure collection and processing of personal data. Such regulated documents can be used to elicit legal requirements that are inline with the organizations’ data protection policies. As a prerequisite to elicit a complete set of legal requirements, however, these documents must be compliant with GDPR. Checking the compliance of regulated documents entirely manually is a laborious and error-prone task. As we elaborate below, this dissertation investigates utilizing artificial intelligence (AI) technologies to provide automated support for compliance checking against GDPR. • AI-enabled Automation for Compliance Checking of PPs: PPs are technical documents stating the multiple privacy-related requirements that a system should satisfy in order to help individuals make informed decisions about sharing their personal data. We devise an automated solution that leverages natural language processing (NLP) and machine learning (ML), two sub-fields of AI, for checking the compliance of PPs against the applicable provisions in GDPR. Specifically, we create a comprehensive conceptual model capturing all information types pertinent to PPs and we further define a set of compliance criteria for the automated compliance checking of PPs. • NLP-based Automation for Compliance Checking of DPAs: DPAs are legally binding agreements between different organizations involved in the collection and processing of personal data to ensure that personal data remains protected. Using NLP semantic analysis technologies, we develop an automated solution that checks at phrasal-level the compliance of DPAs against GDPR. Our solution is able to provide not only a compliance assessment, but also detailed recommendations about avoiding GDPR violations. • ML-enabled Automation for Compliance Checking of DPAs: To understand how different representations of GDPR requirements and different enabling technologies fare against one another, we develop an automated solution that utilizes a combination of conceptual modeling and ML. We further empirically compare the resulting solution with our previously proposed solution, which uses natural language to represent GDPR requirements and leverages rules alongside NLP semantic analysis for the automated support

NLP-based Automated Compliance Checking of Data Processing Agreements against GDPR

Processing personal data is regulated in Europe by the General Data Protection Regulation (GDPR) through data processing agreements (DPAs). Checking the compliance of DPAs contributes to the compliance verification of software systems as DPAs are an important source of requirements for software development involving the processing of personal data. However, manually checking whether a given DPA complies with GDPR is challenging as it requires significant time and effort for understanding and identifying DPA-relevant compliance requirements in GDPR and then verifying these requirements in the DPA. In this paper, we propose an automated solution to check the compliance of a given DPA against GDPR. In close interaction with legal experts, we first built two artifacts: (i) the "shall" requirements extracted from the GDPR provisions relevant to DPA compliance and (ii) a glossary table defining the legal concepts in the requirements. Then, we developed an automated solution that leverages natural language processing (NLP) technologies to check the compliance of a given DPA against these "shall" requirements. Specifically, our approach automatically generates phrasal-level representations for the textual content of the DPA and compares it against predefined representations of the "shall" requirements. Over a dataset of 30 actual DPAs, the approach correctly finds 618 out of 750 genuine violations while raising 76 false violations, and further correctly identifies 524 satisfied requirements. The approach has thus an average precision of 89.1%, a recall of 82.4%, and an accuracy of 84.6%. Compared to a baseline that relies on off-the-shelf NLP tools, our approach provides an average accuracy gain of ~20 percentage points. The accuracy of our approach can be improved to ~94% with limited manual verification effort.Comment: 24 pages, 5 figures, 10 tables, 1 Algorithm, TS

AI-enabled Automation for Completeness Checking of Privacy Policies

Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall

Análise descritiva das prevalências e incidências globais de cárie dentária não tratada entre 1990 e 2019

Resumo não disponível

Development of image analysis methods to evaluate barley / malt grain size

It is known that the barley / malt grain size is an important factor regarding the uniformity of malting process and hence the brewery process. For that purpose an image processing and analysis system was built for the evaluation of grain / malt size, on the ImageJ public domain platform. A programme was developed for the barley / malt images treatment and determination of several morphological parameters as well as the grain size distribution. The results showed that for the Prestige and Scarlett barley varieties good correlations could be obtained between the standard weight distribution and the proposed image analysis method. For the Esterel malt and barley as well as for the Nevada barley reasonable to good correlations were also obtained upon the introduction of a density correction factor

Vascular flora in dry-shrub and wet grassland Cerrado seven years after a fire, Federal District, Brasil

ABSTRACTStudies of temporal dynamics for grassland sites report that fire suppression plays a crucial role in floristic changes. The objective of this study was to verify whrther after seven years without fire, communities showed variations in terms of composition, life forms pollination and dispersal syndromes. The first survey (T0) was conducted from September 1999 to October 2000, while the second (T1) took place from August 2006 to August 2007. The floristic results in T1 were compared with the survey in T0 through the Sorensen similarity index and Chi-square tests. Over time, there were differences in the composition,life forms and pollination and dispersion syndromes. The evidence of changes suggests that the frequency of the regime can be considered the fire regime can be considered the main agent for change in the flora of these communities

ML-based Compliance Verification of Data Processing Agreements against GDPR

Most current software systems involve processing personal data, an activity that is regulated in Europe by the general data protection regulation (GDPR) through data processing agreements (DPAs). Developing compliant software requires adhering to DPA-related requirements in GDPR. Verifying the compliance of DPAs entirely manually is however time-consuming and error-prone. In this paper, we propose an automation strategy based on machine learning (ML) for checking GDPR compliance in DPAs. Specifically, we create, based on existing work, a comprehensive conceptual model that describes the information types pertinent to DPA compliance. We then develop an automated approach that detects breaches of compliance by predicting the presence of these information types in DPAs. On an evaluation set of 30 real DPAs, our approach detects 483 out of 582 genuine violations while introducing 93 false violations, achieving thereby a precision of 83.9% and recall of 83.0%. We empirically compare our approach against an existing approach which does not employ ML but relies on manually-defined rules. Our results indicate that the two approaches perform on par. Therefore, to select the right solution in a given context, we discuss differentiating factors like the availability of annotated data and legal experts, and adaptation to regulation changes

Outpatient percutaneous treatment of deep venous malformations using pure ethanol at low doses under local anesthesia

INTRODUCTION: Venous malformations are the most frequent vascular malformation. Deep venous malformations are located in subcutaneous tissue or in the muscles. Percutaneous sclerotherapy is the treatment of choice, and the use of ethanol at low doses has not yet been described. OBJECTIVE: To analyze the results of treating Deep venous malformations patients with low doses of ethanol. METHODS: Thirty-nine patients treated between July 1995 and June 2007 were followed up prospectively over a median period of 18 months. Twenty-nine were female (74.4%) and 10 were male (25.6%), with ages ranging from 11 to 59 years (median of 24 years). All of the lesions affected limbs, and the main symptom reported was pain (97.4%). Each patient underwent fortnightly alcohol application sessions under local anesthesia on an outpatient basis. The lesions were classified into three groups according to size using nuclear magnetic resonance imaging: small, up to 3 cm (4 patients); medium, between 3 and 15 cm (27 patients); and large, greater than 15 cm (8 patients). RESULTS: The symptoms completely disappeared in 14 patients (35.9%) and improved in 24 (61.5%). The lesion size reduced to zero in 6 patients (15.4%) and decreased in 32 (82%). The median number of sessions was 7. There were no complications in 32 patients (82%), while 3 presented local paresthesia (7.7%), 2 superficial trombophlebites (5.1%), 1 skin ulcer (2.6%), and 1 case of hyperpigmentation (2.6%). CONCLUSION: Outpatient treatment for Deep venous malformations patients using ethanol at low doses was effective, with a low complication rate

Evaluación de la calidad de vida en clientes con dolor crónico isquémico

The evaluation of quality of life (QOL) faced with chronic ischemic pain involves the clients in their subjectivity and multidimensionality. This descriptive study aimed to evaluate the quality of life of clients who presented chronic ischemic pain. A total of 100 clients of hospital institutes participated in the study. The instrument used to assess pain was an 11 point numerical scale, and to assess the quality of life, the World Health Organization Quality of Life-abbreviated questionnaire. The arithmetic mean for chronic pain was 5.59±3.16 points. The means for quality of life were: in the physical domain, 44.75±16.98; in the overall domain, 50.0±22.40; in the environment, 55.06±13.51, in the psychological, 56.21±17.19 and in the social domain, 68.33±21.84. Thus, the physical domain was, among the areas analyzed, the one which presented a greater impact on the quality of life of the clients with chronic ischemic pain.A avaliação da qualidade de vida (QV), frente à dor crônica isquêmica, envolve o cliente na sua subjetividade e multidimensionalidade. Este estudo descritivo teve como objetivo avaliar a qualidade de vida de clientes que manifestaram dor crônica isquêmica. Participaram da pesquisa 100 clientes de instituições hospitalares. O instrumento aplicado para avaliar a dor foi a escala numérica de 11 pontos e, para a qualidade de vida, o questionário World Health Organization Quality of Life-abreviado. A média aritmética para a dor crônica foi de 5,59±3,16 pontos. As médias para a qualidade de vida foram: no domínio físico, 44,75±16,98; no global, 50,0±22,40; no ambiental, 55,06±13,51; no psicológico, 56,21±17,19 e, no social, 68,33±21,84. Assim, o domínio físico foi, dentre os domínios analisados, o que apresentou maior impacto sobre a qualidade de vida dos clientes com dor crônica isquêmica.La evaluación de la calidad de vida (CV) frente al dolor crónico isquémico debe considerar la subjetividad del cliente y las múltiples dimensiones envueltas. Este estudio descriptivo tuvo como objetivo evaluar la calidad de vida de clientes que manifestaron dolor crónico isquémico. Participaron de la investigación 100 clientes de instituciones hospitalarias. El instrumento aplicado para evaluar el dolor fue la escala numérica de 11 puntos y para la calidad de vida, el cuestionario World Health Organization Quality of Life abreviado. El promedio aritmético para el dolor crónico fue de 5,59±3,16 puntos. Los promedios para la calidad de vida fueron: en el dominio físico, 44,75±16,98; en el global, 50,0±22,40; en el ambiental, 55,06±13,51; en el psicológico, 56,21±17,19 y en el social, 68,33±21,84. Así, el dominio físico fue, entre los dominios analizados, el que presentó un mayor impacto sobre la calidad de vida de los clientes con dolor crónico isquémico