scholarly journals Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts

2017 ◽  
Author(s):  
Dominik Jäckle ◽  
Florian Stoffel ◽  
Sebastian Mittelstädt ◽  
Daniel A. Keim ◽  
Harald Reiterer
Keyword(s):  
Crime Data ◽  
Domain Experts

Utilizing Incident-Based Crime Data to Inform Strategic Interventions

2016 ◽  
Vol 17 (1) ◽  
pp. 3-27
Author(s):  
Jason Rydberg ◽  
Rebecca Stone ◽  
Edmund F. McGarrell
Keyword(s):  
Reporting System ◽  
Excess Risk ◽  
Public Health Approach ◽  
Least Squares Regression ◽  
Crime Data ◽  
Targeted Interventions ◽  
Aggravated Assault ◽  
System A ◽  
High Crime ◽  
Demographic Subgroups

A public health approach to violence prevention involves the empirical identification of groups and communities at the highest risk for violence to inform targeted interventions. We demonstrate the utility of complete incident-level crime data toward this end. Data for 32,056 unique incidents involving homicide, aggravated assault, and robbery were extracted from the 2013 Michigan Incident Crime Reporting system, a statewide National Incident-Based Reporting System (NIBRS) data system. Differential victimization rates were calculated across demographic subgroups and jurisdictions to identify patterns in risk. Two-stage least squares regression models were estimated to examine correlates of variation in excess risk. Analyses identified young Black males and females at relatively high risk for violent victimization, and that this risk was amplified within cities with disproportionately high crime rates. Multivariate models suggested concentrated disadvantage as the most stable correlate of variation in excess risk across Michigan cities and towns. The results highlight the importance of expanding NIBRS adoption and the deployment of focused interventions involving both short-term enforcement and long-term social reinvestment.

scholarly journals CSR Initiatives and Practices: Empirical Evidence From Indian Metal and Mining Companies

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110326
Author(s):  
Ajay K. Singal
Keyword(s):  
Environmental Sustainability ◽  
Annual Reports ◽  
Local Domain ◽  
Internal Governance ◽  
Domain Experts ◽  
Analysis Technique ◽  
Extractive Sector ◽  
Second Tier ◽  
Corporate Social ◽  
Text Network Analysis

This study investigates the corporate social responsibility (CSR) discourse on community and environment by Indian metal and mining (extractive) sector. Specifically, we examine the change in internal governance and external implementation mechanisms in response to affirmative CSR policy actions. Applying text network analysis technique on CSR related expenditures provided in the annual reports and CSR annexures (2014–2018), our study reveals that CSR discourse of extractive firms improved significantly and became more focused after the introduction of post-affirmative policy. CSR initiatives in the extractive sector are primarily focused toward local social development, with little emphasis on the environmental sustainability. Furthermore, companies have adopted two-tier governance structures for managing CSR. The top tier comprises board members who formulate the CSR programs, while the second tier has executives responsible for the implementation. Another tier of governance involving local domain experts is emerging. The three-tier implementation mechanisms give firms a tighter control on spending and enhance the effectiveness of initiatives. We present the results visually in the form of network graphs.

scholarly journals Evaluating the Bovine Tuberculosis Eradication Mechanism and Its Risk Factors in England’s Cattle Farms

2021 ◽  
Vol 18 (7) ◽  
pp. 3451
Author(s):  
Tabassom Sedighi ◽  
Liz Varga
Keyword(s):  
Risk Factors ◽  
Bovine Tuberculosis ◽  
Animal Health ◽  
Dynamic Bayesian Network ◽  
Policy Makers ◽  
Domain Experts ◽  
Actual Experience ◽  
Complex Evaluation ◽  
Risk Areas ◽  
Cattle Farms

Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms.

Regulations and Norms for Reuse of Residual Clinical Biospecimens and Health Data

2021 ◽  
pp. 019394592110292
Author(s):  
Elizabeth E. Umberfield ◽  
Sharon L. R. Kardia ◽  
Yun Jiang ◽  
Andrea K. Thomer ◽  
Marcelline R. Harris
Keyword(s):  
Informed Consent ◽  
Scoping Review ◽  
Real World ◽  
Genetic Information ◽  
Final Analysis ◽  
Health Data ◽  
Federal Regulations ◽  
Secondary Research ◽  
Domain Experts ◽  
Key Issues

Nurse scientists are increasingly interested in conducting secondary research using real world collections of biospecimens and health data. The purposes of this scoping review are to (a) identify federal regulations and norms that bear authority or give guidance over reuse of residual clinical biospecimens and health data, (b) summarize domain experts’ interpretations of permissions of such reuse, and (c) summarize key issues for interpreting regulations and norms. Final analysis included 25 manuscripts and 23 regulations and norms. This review illustrates contextual complexity for reusing residual clinical biospecimens and health data, and explores issues such as privacy, confidentiality, and deriving genetic information from biospecimens. Inconsistencies make it difficult to interpret, which regulations or norms apply, or if applicable regulations or norms are congruent. Tools are necessary to support consistent, expert-informed consent processes and downstream reuse of residual clinical biospecimens and health data by nurse scientists.

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

scholarly journals Robust diagnostic classification via Q-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Victor Ardulov ◽  
Victor R. Martinez ◽  
Krishna Somandepalli ◽  
Shuting Zheng ◽  
Emma Salzman ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Decision Function ◽  
Diagnostic Classification ◽  
Decision Making Process ◽  
Domain Experts ◽  
Q Learning ◽  
School Aged Children ◽  
Hyperactivity Disorder ◽  
Clinical Diagnoses ◽  
Learning Frameworks

AbstractMachine learning (ML) models have demonstrated the power of utilizing clinical instruments to provide tools for domain experts in gaining additional insights toward complex clinical diagnoses. In this context these tools desire two additional properties: interpretability, being able to audit and understand the decision function, and robustness, being able to assign the correct label in spite of missing or noisy inputs. This work formulates diagnostic classification as a decision-making process and utilizes Q-learning to build classifiers that meet the aforementioned desired criteria. As an exemplary task, we simulate the process of differentiating Autism Spectrum Disorder from Attention Deficit-Hyperactivity Disorder in verbal school aged children. This application highlights how reinforcement learning frameworks can be utilized to train more robust classifiers by jointly learning to maximize diagnostic accuracy while minimizing the amount of information required.

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