Visilant: Visual Support for the Exploration and Analytical Process Tracking in Criminal Investigations

• URL: http://arxiv.org/abs/2009.09082v1
• Date: Mon, 21 Sep 2020 09:24:20 GMT
• Title: Visilant: Visual Support for the Exploration and Analytical Process Tracking in Criminal Investigations
• Authors: Krist\'ina Z\'akop\v{c}anov\'a, Marko \v{R}eh\'a\v{c}ek, Jozef B\'atrna, Daniel Plakinger, Sergej Stoppel, Barbora Kozl\'ikov\'a
• Abstract summary: Visilant is a web-based tool for the exploration and analysis of criminal data guided by the proposed design. The tool was evaluated by senior criminology experts within two sessions and their feedback is summarized in the paper.
• Score: 1.8594711725515676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The daily routine of criminal investigators consists of a thorough analysis of highly complex and heterogeneous data of crime cases. Such data can consist of case descriptions, testimonies, criminal networks, spatial and temporal information, and virtually any other data that is relevant for the case. Criminal investigators work under heavy time pressure to analyze the data for relationships, propose and verify several hypotheses, and derive conclusions, while the data can be incomplete or inconsistent and is changed and updated throughout the investigation, as new findings are added to the case. Based on a four-year intense collaboration with criminalists, we present a conceptual design for a visual tool supporting the investigation workflow and Visilant, a web-based tool for the exploration and analysis of criminal data guided by the proposed design. Visilant aims to support namely the exploratory part of the investigation pipeline, from case overview, through exploration and hypothesis generation, to the case presentation. Visilant tracks the reasoning process and as the data is changing, it informs investigators which hypotheses are affected by the data change and should be revised. The tool was evaluated by senior criminology experts within two sessions and their feedback is summarized in the paper. Additional supplementary material contains the technical details and exemplary case study.

Related papers

• Advancing Crime Linkage Analysis with Machine Learning: A Comprehensive Review and Framework for Data-Driven Approaches [0.0]
  Crime linkage is the process of analyzing criminal behavior data to determine whether a pair or group of crime cases are connected or belong to a series of offenses. This study aims to understand the challenges faced by machine learning approaches in crime linkage and to support foundational knowledge for future data-driven methods.
  arXiv  Detail & Related papers  (2024-10-30T18:22:45Z)
• A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations [23.87336875544181]
  We redefinition causal data into three categories: definite data, semi-definite data, and indefinite data. Definite data pertains to statistical data used in conventional causal scenarios, while semi-definite data refers to a spectrum of data formats germane to deep learning. Indefinite data is an emergent research sphere inferred from the progression of data forms by us.
  arXiv  Detail & Related papers  (2023-11-02T01:31:42Z)
• An Intent Taxonomy of Legal Case Retrieval [43.22489520922202]
  Legal case retrieval is a special Information Retrieval(IR) task focusing on legal case documents. We present a novel hierarchical intent taxonomy of legal case retrieval. We reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
  arXiv  Detail & Related papers  (2023-07-25T07:27:32Z)
• A Principled Design of Image Representation: Towards Forensic Tasks [75.40968680537544]
  We investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. At the theoretical level, we propose a new representation framework for forensics, called Dense Invariant Representation (DIR), which is characterized by stable description with mathematical guarantees. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors.
  arXiv  Detail & Related papers  (2022-03-02T07:46:52Z)
• Yes-Yes-Yes: Donation-based Peer Reviewing Data Collection for ACL Rolling Review and Beyond [58.71736531356398]
  We present an in-depth discussion of peer reviewing data, outline the ethical and legal desiderata for peer reviewing data collection, and propose the first continuous, donation-based data collection workflow. We report on the ongoing implementation of this workflow at the ACL Rolling Review and deliver the first insights obtained with the newly collected data.
  arXiv  Detail & Related papers  (2022-01-27T11:02:43Z)
• A Comparative Review of Recent Few-Shot Object Detection Algorithms [0.0]
  Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem. Recent studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions.
  arXiv  Detail & Related papers  (2021-10-30T07:57:11Z)
• Deep Learning Schema-based Event Extraction: Literature Review and Current Trends [60.29289298349322]
  Event extraction technology based on deep learning has become a research hotspot. This paper fills the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models.
  arXiv  Detail & Related papers  (2021-07-05T16:32:45Z)
• Explainable Adversarial Attacks in Deep Neural Networks Using Activation Profiles [69.9674326582747]
  This paper presents a visual framework to investigate neural network models subjected to adversarial examples. We show how observing these elements can quickly pinpoint exploited areas in a model.
  arXiv  Detail & Related papers  (2021-03-18T13:04:21Z)
• Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events [7.219077740523683]
  Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world. This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case. Leveraging previously processed digital forensic cases and their component artefact relevancy classifications can facilitate an opportunity for training automated artificial intelligence based evidence processing systems.
  arXiv  Detail & Related papers  (2020-12-02T14:14:26Z)
• Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process [63.75363908696257]
  We review the methods that have been applied to network data with the purpose of developing an intrusion detector. We discuss the techniques used for the capture, preparation and transformation of the data, as well as, the data mining and evaluation methods. As a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.
  arXiv  Detail & Related papers  (2020-01-27T11:21:05Z)
• Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction [48.1813701535167]
  We perform crime prediction exploiting the cross-type and-temporal correlations of urban crimes. We propose a coherent framework to mathematically model these correlations for crime prediction. Further experiments have been conducted to understand the importance of different correlations in crime prediction.
  arXiv  Detail & Related papers  (2020-01-20T00:34:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.