Natural Language Processing and e-Government: Crime Information Extraction from Heterogeneous Data Sources

Much information that could help solve and prevent crimes is never gathered because the reporting methods available to citizens and law enforcement personnel are not optimal. Detectives do not have sufficient time to interview crime victims and witnesses. Moreover, many victims and witnesses are too scared or embarrassed to report incidents. We are developing an interviewing system that will help collect such information. We report here on one component, the crime information extraction module, which uses natural language processing to extract crime information from police reports, newspaper articles, and victims’ and witnesses’ crime narratives. We tested our approach with two types of document: police and witness narrative reports. Our algorithms extract crime-related information, namely weapons, vehicles, time, people, clothes, and locations. We achieved high precision (96%) and recall (83%) for police narrative reports and comparable precision (93%) but somewhat lower recall (77%) for witness narrative reports. The difference in recall was significant at p \u3c .05. We then used a spell checker to evaluate if this would help with witness narrative processing. We found that both precision (94 %) and recall (79%) improved slightly

Enabling Synergy between Psychology and Natural Language Processing for e-Government: Crime Reporting and Investigative Interview System

We are developing an automated crime reporting and investigative interview system. The system incorporates cognitive interview techniques to maximize witness memory recall, and information extraction technology to extract and annotate crime entities from witness narratives and interview responses. Evaluations of the IE components of the system show that it captures 70 to 77% of information from witness narratives with 93 to 100% precision. Our development goal is for the system to approximate progressively the performance effectiveness of a human investigative interviewer and to generate graphical visualizations of crime report information

Crime Information Extraction from Police and Witness Narrative Reports

To solve crimes, investigators often rely on interviews with witnesses, victims, or criminals themselves. The interviews are transcribed and the pertinent data is contained in narrative form. To solve one crime, investigators may need to interview multiple people and then analyze the narrative reports. There are several difficulties with this process: interviewing people is time consuming, the interviews - sometimes conducted by multiple officers - need to be combined, and the resulting information may still be incomplete. For example, victims or witnesses are often too scared or embarrassed to report or prefer to remain anonymous. We are developing an online reporting system that combines natural language processing with insights from the cognitive interview approach to obtain more information from witnesses and victims. We report here on information extraction from police and witness narratives. We achieved high precision, 94% and 96%, and recall, 85% and 90%, for both narrative types

Diagnóstico y tratamiento de la Enfermedad de Addison; ejemplos de su manejo clínico

La insuficiencia adrenal primaria o enfermedad de Addison, cursa con la producción insuficiente de glucocorticoides y en ocasiones mineralocorticoides. Tiene diversas manifestaciones dependiendo de la causa. Un alto porcentaje de pacientes portadores de insuficiencia adrenal primaria autoinmune cursan con dos o más desórdenes órganoespecíficos, dando lugar a los síndromes autoinmunes poliendocrinos. Debido a la variedad de manifestaciones con las que puede cursar esta enfermedad, es necesario realizar estudios para descartar disfunciones glandulares a otros niveles y finalmente llegar a la terapia de reemplazo necesaria. En este artículo, se presenta el manejo y la evolución de casos de enfermedad de Addison de presentación variable

Artificial Intelligence and Visual Analytics: A Deep-Learning Approach to Analyze Hotel Reviews & Responses

With a growing number of online reviews, consumers often rely on these reviews to make purchase decisions. However, little is known about managerial responses to online hotel reviews. This paper reports on a framework to integrate visual analytics and machine learning techniques to investigate whether hotel managers respond to positive and negative reviews differently and how to use a deep-learning approach to prioritize responses. In this study, forty 4- and 5-star hotels in London with 91,051 reviews and 70,397 responses were collected and analyzed. Visual analyses and machine learning were conducted. The results indicate most hotels (72.5%) showing no preference to respond to positive and negative reviews. Our proposed deep-learning approach outperformed existing algorithms to prioritize responses

Knowledge-Enriched Visual Storytelling

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.Comment: AAAI 202

Location-Aware Visual Question Generation with Lightweight Models

This work introduces a novel task, location-aware visual question generation (LocaVQG), which aims to generate engaging questions from data relevant to a particular geographical location. Specifically, we represent such location-aware information with surrounding images and a GPS coordinate. To tackle this task, we present a dataset generation pipeline that leverages GPT-4 to produce diverse and sophisticated questions. Then, we aim to learn a lightweight model that can address the LocaVQG task and fit on an edge device, such as a mobile phone. To this end, we propose a method which can reliably generate engaging questions from location-aware information. Our proposed method outperforms baselines regarding human evaluation (e.g., engagement, grounding, coherence) and automatic evaluation metrics (e.g., BERTScore, ROUGE-2). Moreover, we conduct extensive ablation studies to justify our proposed techniques for both generating the dataset and solving the task.Comment: EMNLP 202

A Power-Efficient Multiband Planar USB Dongle Antenna for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) had been applied in Internet of Things (IoT) and in Industry 4.0. Since a WSN system contains multiple wireless sensor nodes, it is necessary to develop a low-power and multiband wireless communication system that satisfies the specifications of the Federal Communications Commission (FCC) and the Certification European (CE). In a WSN system, many devices are of very small size and can be slipped into a Universal Serial Bus (USB), which is capable of connecting to wireless systems and networks, as well as transferring data. These devices are widely known as USB dongles. This paper develops a planar USB dongle antenna for three frequency bands, namely 2.30–2.69 GHz, 3.40–3.70 GHz, and 5.15–5.85 GHz. This study proposes a novel antenna design that uses four loops to develop the multiband USB dongle. The first and second loops construct the low and intermediate frequency ranges. The third loop resonates the high frequency property, while the fourth loop is used to enhance the bandwidth. The performance and power consumption of the proposed multiband planar USB dongle antenna were significantly improved compared to existing multiband designs