Applications of data science in policing: VeriPol as an investigation support tool

Data Science is an interdisciplinary field involving the development of processes and systems to extract knowledge and understanding from data in different formats and from different sources. Considering the large amount of data generated and managed by public safety agencies, Data Science applications in the police sector are numerous. More important are the advantages that the different applications of Data Science could provide the police on issues such as the optimization of resources, the increase of efficiency and effectiveness, the modernization and its exemplariness when compared with other institutions. In this paper we present different potential applications fields of Data Science for the police. In addition, we focus on the case of VeriPol, a tool for automatic detection of false violent robbery reports, currently under development by the Spanish National Police. In particular, we illustrate a detailed analysis of the results of a recent pilot study aimed at assessing the effectiveness of the tool

Make it personal: a social explanation system applied to group recommendations

Recommender systems help users to identify which items from a variety of choices best match their needs and preferences. In this context, explanations act as complementary information that can help users to better comprehend the system’s output and to encourage goals such as trust, confidence in decision-making or utility. In this paper we propose a Personalized Social Individual Explanation approach (PSIE). Unlike other expert systems the PSIE proposal novelly includes explanations about the system’s group recommendation and explanations about the group’s social reality with the goal of inducing a positive reaction that leads to a better perception of the received group recommendations. Among other challenges, we uncover a special need to focus on “tactful” explanations when addressing users’ personal relationships within a group and to focus on personalized reassuring explanations that encourage users to accept the presented recommendations. Besides, the resulting intelligent system significatively increases users’ intent (likelihood) to follow the recommendations, users’ satisfaction and the system’s efficiency and trustworthiness

Applications of data science in policing: VeriPol as an investigation support tool

Data Science is an interdisciplinary field involving the development of processes and systems to extract knowledge and understanding from data in different formats and from different sources. Considering the large amount of data generated and managed by public safety agencies, Data Science applications in the police sector are numerous. More important are the advantages that the different applications of Data Science could provide the police on issues such as the optimization of resources, the increase of efficiency and effectiveness, the modernization and its exemplariness when compared with other institutions. In this paper we present different potential applications fields of Data Science for the police. In addition, we focus on the case of VeriPol, a tool for automatic detection of false violent robbery reports, currently under development by the Spanish National Police. In particular, we illustrate a detailed analysis of the results of a recent pilot study aimed at assessing the effectiveness of the tool

The BIG CHASE: A decision support system for client acquisition applied to financial networks

Bank agencies daily store a huge volume of data regarding clients and their operations. This information, in turn, can be used for marketing purposes to acquire new clients or sell products to existing clients. A Decision Support System (DSS) can help a manager to decide the sequence of clients to contact to reach a designed target. In this paper we present the BIG CHASE, a DSS that translates bank data into a reliability graph. This graph models relationships based on a probability of traversal function that includes social measures. The proposed DSS, developed in close collaboration with Banco Santander, S.A., fits the parameters of the probability function to explicit solution evaluations given by experts by means of a specifically designed Projected Gradient Descent algorithm. The fitted probability function determines the reliabilities associated to the edges of the graph. An optimization procedure tailored to be efficient on very large sparse graphs with millions of nodes and edges identifies the most reliable sequence of clients that a manager should contact to reach a specific target. The BIG CHASE has been tested with a case study on real data that includes Banco Santander, S.A. 2015 Spain bank records. Experimental results show that the proposed DSS is capable of modeling the experts' evaluations into probability function with a small error

Personality and Social Trust in Group Recommendations

In this paper we describe some new ideas to improve recommendations to groups of people. Our approach maximizes the global satisfaction for the group taking into account people personality and the social relationships among people in the group. We present some results with two cases of study based on the movie recommendation domain with heterogeneous groups. The first case study uses synthetically generated groups of people to test how the group composition affects the accuracy of the recommendation. Our second case study uses real users and groups where the topology of the groups is based on a social network. This second case of study with real users confirms the wide conclusions of the preliminary experiment with synthetic data, which allows us to conclude that it is possible to realize trustworthy experiments with synthetic data.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEpu

An architecture and functional description to integrate social behaviour knowledge into group recommender systems

In this paper we consider the research challenges of generating a set of recommendations that will satisfy a group of users with potentially competing interests. We re view different ways of combining the preferences of differ ent users and propose an approach that takes into account the social behaviour within a group. Our method, named delegation-based prediction method, includes an analysis of the group characteristics, such as size, structure, personal ity of its members in conflict situations, and trust between group members. A key element in this paper is the use of social information available in the Web to make enhanced recommendations to groups. We propose a generic architec ture named ARISE (Architecture for Recommendations In cluding Social Elements) and describe, as a case study, our Facebook application HappyMovie: a group recommender system that is designed to provide assistance to a group of friends that might be selecting which movie to watch on a cinema outing. We evaluate the performance (compared with the real group decision) of different recommenders that use increasing levels of social behaviour knowledge.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEpu

Biological insecticides for the control Spodoptera frugiperda Smith, its incidence on yield

The work was carried out in farmers' fields of located in the area of Portoviejo, province of Manabí; with the objective of evaluating two biological insecticides, Methakill and Baukill with doses of 5, 10 and 15 mL L-1 of water for the control of S. frugiperda, being evaluated the populations of the insect before and after the application of the same. A Full Random Block Design was used, with three replicates. The chemical treatment (Lambda Cyhalothrin + Tiametoxan 1.5 mL L-1 of water) presented the lowest averages of damage (6.47 %), followed by the treatment that consisted of the spraying of Methakill in doses of 15 mL L-1 water with 10.57 % damage. The Control treatment had the highest percentages of involvement with 27.78 %. The highest yields were obtained in the treatments where Lambda Cyhalothrin + Tiametoxan 1.5 mL L-1 water and Methakill 15 mL L-1 water were applied, statistically equal to each other with 7989.24 and 6919.43 kg ha-1 respectively. The best Marginal Return Rate was obtained using the Methakill treatment 15 mL L-1 of water with 1043.45 %