Search Results (1,274)

Traditional police interrogation processes remain largely time-consuming and reliant on substantial human effort for both analysis and documentation. Intuition Artificial Intelligence (INTU-AI) is a Windows application designed to digitalize the administrative workflow associated with police interrogations, while enhancing procedural efficiency through the integration of AI-driven emotion recognition models. The system employs a multimodal approach that captures and analyzes emotional states using three primary vectors: Facial Expression Recognition (FER), Speech Emotion Recognition (SER), and Text-based Emotion Analysis (TEA). This triangulated methodology aims to identify emotional inconsistencies and detect potential suppression or concealment of affective responses by interviewees. INTU-AI serves as a decision-support tool rather than a replacement for human judgment. By automating bureaucratic tasks, it allows investigators to focus on critical aspects of the interrogation process. The system was validated in practical training sessions with inspectors and with a 12-question questionnaire. The results indicate a strong acceptance of the system in terms of its usability, existing functionalities, practical utility of the program, user experience, and open-ended qualitative responses. Full article

First responders face adverse health effects because they regularly encounter stressful situations and potentially traumatic events. Peer support programs have emerged as a method to reduce these adverse outcomes. A growing interest in peer programs exists despite a restricted body of research in this field. Additionally, the current research on this topic faces significant conceptual and methodological shortcomings. This paper conducts an extensive analysis of present peer support research gaps before proposing future study directions to improve our understanding of this intervention. Full article

Terminal forensics in large mobile networks is a vital activity for identifying compromised devices and analyzing malicious actions. In contrast, the study described here begins with the domain of terminal forensics as the primary focus, rather than the threat itself. This paper proposes a new multi-criteria decision-making (MCDM) model that integrates complex picture fuzzy sets (CPFS) with the combinative distance-based assessment (CODAS), referred to throughout as complex picture fuzzy CODAS (CPF-CODAS). The aim is to assist in forensic analysis for detecting mobile botnet command and control (C&C) systems. The CPF-CODAS model accounts for the uncertainty, hesitation, and complex numerical values involved in expert decision-making, using degrees of membership as positive, neutral, and negative values. An illustrative forensic case study is constructed where three mobile devices are evaluated by three cybersecurity professionals based on six key parameters related to botnet activity. The results demonstrate that the model can effectively distinguish suspicious devices and support the use of the CPF-CODAS approach in terminal forensics of mobile networks. The robustness, symmetry, and advantages of this model over existing MCDM methods are confirmed through sensitivity and comparison analyses. In conclusion, this paper introduces a novel probabilistic decision-support tool that digital forensic specialists can incorporate into their workflow to proactively identify and prevent actions of mobile botnet C&C servers. Full article

Intimate partner violence (IPV) has far-reaching health and social consequences, particularly for survivors experiencing polyvictimization—multiple forms of IPV such as physical, emotional, and sexual abuse. This study examined help-seeking behaviors and the perceived helpfulness of formal support sources (police, medical professionals, and psychologists) among a nationally representative sample of 2387 IPV survivors drawn from the 2010 National Intimate Partner and Sexual Violence Survey (NISVS) in the United States. Latent class analysis identified three distinct polyvictimization profiles: Coercive Control and Psychological Aggression (CCPA), Psychological and Physical Violence (PPV), and Multiple Violence (MV). Survivors’ patterns of formal help-seeking varied significantly by gender, sexual orientation, socioeconomic status, and type of victimization. Psychologists were the most commonly contacted and perceived as the most helpful overall, though disparities emerged. Female survivors and those with less severe victimization were more likely to rate support as helpful, whereas male and sexual/gender minority (SGM) survivors, particularly those facing severe or multiple forms of violence, were less likely to find formal sources helpful—especially law enforcement. These findings highlight the need for more inclusive, culturally competent, and trauma-informed services tailored to the diverse experiences of IPV survivors. Full article

This study investigates the evolution of cybercrime in Greece by analyzing data from the Cyber Crime Division of the Hellenic Police. By combining 2023 statistics with earlier national and international data (e.g., Europol, FBI), this study presents a comprehensive 15-year view of cybercrime trends. Key findings highlight a persistent rise in cyber incidents, with financial fraud as the most common type. Other major threats include unauthorized system access, data breaches, and crimes targeting vulnerable populations. The study assesses national legislation aligned with EU directives and outlines stakeholder roles. It underscores the need for adaptive legal frameworks, inter-agency cooperation, and public awareness to mitigate Greece’s growing cybersecurity challenges. Full article

Traditional training methods for first responders are often limited by time, resources, and safety constraints, which reduces their consistency and effectiveness. This study focused on two main issues: whether exposure to virtual reality training scenarios induces measurable physiological changes in heart rate and heart rate variability, and whether these responses differ between police and firefighter contexts. The aim of this study was to explore the integration of virtual reality technologies into responder training and to evaluate how biomedical monitoring can be used to assess training effectiveness. A pilot measurement was conducted with ten participants who completed systematic crime scene investigation scenarios in both domains. Heart activity was continuously recorded using a wearable sensor and analyzed for heart rate and heart rate variability parameters, while cognitive load and task performance were also assessed. The collected data were statistically evaluated using tests of normality and paired comparisons between baseline and virtual reality phases. The results showed a significant increase in heart rate and a decrease in heart rate variability during virtual reality exposure compared to baseline, with higher cognitive load and success rates in police scenarios compared to firefighter scenarios. These findings indicate that virtual reality scenarios can elicit measurable psychophysiological responses and highlight the potential of combining immersive technologies with biomedical monitoring for the development of adaptive and effective training methods for first responders. Full article

This study investigates the involvement of the Hellenic Police in the management of natural disasters. The legislation governing police participation in disaster management in Greece is based on the general framework of civil protection policy, outlining the responsibilities assigned to various agencies for handling emergency situations. The role of the Hellenic Police is particularly significant and proactive in both the prevention and management of natural disasters, with specific responsibilities and duties. Key areas of Hellenic Police involvement in disaster management include the following: 1. prevention and public awareness; 2. risk identification and management; 3. evacuation of areas, organized removal and relocation of citizens and traffic management; 4. cooperation and coordination with other authorities and services; 5. support for rescue teams; and 6. security and order in affected areas. Full article

Achieving consensus in group decision-making is both essential and challenging, especially in which non-cooperative behaviors can significantly hinder the process under uncertainty. These behaviors may distort consensus outcomes, leading to increased costs and reduced efficiency. To address this issue, this study proposes a data-driven robust minimum-cost consensus model (MCCM) that accounts for non-cooperative behaviors by leveraging individual adjustment willingness. The model introduces an adjustment willingness function to identify non-cooperative participants during the consensus-reached process (CRP). To handle uncertainty in unit consensus costs, Principal Component Analysis (PCA) and Kernel Density Estimation (KDE) are employed to construct data-driven uncertainty sets. A robust optimization framework is then used to minimize the worst-case consensus cost within these sets, improving the model’s adaptability and reducing the risk of suboptimal decisions. To enhance computational tractability, the model is reformulated into a linear equivalent using the duality theory. Experimental results from a case study on house demolition compensation negotiations in Guiyang demonstrate the model’s effectiveness in identifying and mitigating non-cooperative behaviors. The proposed approach significantly improves consensus efficiency and consistency, while the data-driven robust strategy offers greater flexibility than traditional robust optimization methods. These findings suggest that the model is well-suited for complex real-world group decision-making scenarios under uncertainty. Full article

The ability of police officers to make correct decisions under emotional stress is critical, as errors in high-pressure situations can have severe legal and physical consequences. This study aims to evaluate the neurophysiological responses of police academy cadets during stressful decision-making scenarios and to predict individual stress levels from those responses. Fifty-eight police academy cadets from three cohorts watched a custom-made, naturalistic video scene and then chose the appropriate course of action. Simultaneous 32-channel electroencephalography (EEG) and electrocardiography (ECG) captured brain and heart activity. Event-related potentials (ERPs) and band-specific power features (particularly delta) were extracted, and machine-learning models were trained with nested cross-validation to predict perceived stress scores. Global and broadband EEG activity was suppressed during the video stimulus and did not return to baseline during the cooldown phase. Widespread ERPs and pronounced delta-band dynamics emerged during decision-making, correlating with both cohort rank and self-reported stress. Crucially, a combined EEG + cohort model predicted perceived stress with an out-of-fold R² of 0.32, outperforming EEG-only (R² = 0.23) and cohort-only (R² = 0.17) models. To our knowledge, this is the first study to both characterize EEG dynamics during stressful naturalistic decision tasks and demonstrate their predictive utility. These findings lay the groundwork for neurofeedback-based training paradigms that help officers modulate stress responses and calibrate decision-making under pressure. Full article

This study explored how the death of Mr. George Floyd and the Black Lives Matter protests that followed it influenced emerging adults’ views on racism and racial identity. A mixed-methods study consisting of open-ended and Likert items was used. Two major themes, developing racial awareness and negotiating positionality, described the processes of how Mr. Floyd’s death influenced emerging adults. Quantitative analyses revealed that most participants indicated that the death made them more aware of racism (79.9%), helped them realize how serious racism is (74.1%), and increased their wanting to learn more about race relations (71.3%). Only 8% of participants attributed Mr. Floyd’s death to an unfortunate accident caused by a police officer just doing his job, with White men more likely to hold that view. Black participants reported feeling more racial pride after the death. Quantitative data revealed students’ perceptions, while qualitative data revealed the processes of how those perceptions were formed. We interpreted results using an integrated critical race theory and symbolic interactionism framework. Full article

Background/Objectives: Due to its potent local anesthetic and vasoconstrictive properties, cocaine is sometimes used in otolaryngologic surgical interventions. However, cocaine topical administration is not always adequately documented by practitioners, which can lead to serious legal consequences, particularly in the context of drug-impaired driving (DUID) investigations. This study retrospectively analyzes five road accident cases where cocaine was detected in biological samples after medical interventions. Case descriptions: Following pedestrian–car, or car–car accidents, five distinct patients aged between 30 and 84 years underwent maxillofacial surgery due to significant injuries. Given the severity of the accident and the circumstances, the police requested blood toxicological analysis to determine whether the patients were under the influence of psychoactive substances at the moment of the accidents. Results: The five cases described in this manuscript had blood cocaine concentrations exceeding the Swiss legal limit for drivers (15 µg/L). Since no information was initially provided about the medical use of cocaine after the crash, recreational use of cocaine was suspected. However, subsequent investigations confirmed that the cases involved medical administration. Conclusions: After sinonasal procedures involving the topical application of cocaine, patients may yield positive results on urine and blood drug tests, potentially resulting in serious legal repercussions, including the withdrawal of their driving license. Therefore, practitioners should thoroughly document the medical use of topical cocaine, particularly in DUID cases. These results also raise questions about the benefit–risk ratio of such use, considering that alternatives exist. Full article

Obesity is a major public health concern among police officers, yet the links between occupational stress, disordered eating, and obesity remain unclear. This cross-sectional study examined 496 North Carolina officers to (1) assess severity of occupational stress (posttraumatic stress disorder [PTSD] symptoms, anti-police sentiment, fear of victimization), disordered eating (binge eating and loss-of-control eating), and obesity by county type, region, and sex; (2) evaluate associations between occupational stress and disordered eating; and (3) explore relationships between disordered eating and weight-related measures. Officers completed online surveys, and trained staff measured body mass index (BMI), waist and hip circumferences, and waist-to-hip ratio. Nearly 60 percent of officers were classified as obese (BMI ≥ 30 kg/m²), and over 20 percent reported moderate to severe binge eating. Rural officers reported higher PTSD symptoms, binge eating, and loss-of-control eating than those in urban or suburban areas. Coastal Plain and Piedmont officers had higher BMI and larger waist and hip circumferences than those in the Mountain region. Higher occupational stress was linked to more severe disordered eating, which was associated with greater BMI and adiposity, although the effect sizes were modest. Findings support targeted interventions addressing occupational stress and disordered eating to prevent obesity and enhance officer well-being. Full article

This article solves the anomalies’ operational detection in the network traffic problem for cyber police units by developing an adaptive neural network platform combining a variational autoencoder with continuous stochastic dynamics of the latent space (integration according to the Euler–Maruyama scheme), a continuous–discrete Kalman filter for latent state estimation, and Hotelling’s T² statistical criterion for deviation detection. This paper implements an online learning mechanism (“on the fly”) via the Euler Euclidean gradient step. Verification includes variational autoencoder training and validation, ROC/PR and confusion matrix analysis, latent representation projections (PCA), and latency measurements during streaming processing. The model’s stable convergence and anomalies’ precise detection with the metrics precision is ≈0.83, recall is ≈0.83, the F1-score is ≈0.83, and the end-to-end delay of 1.5–6.5 ms under 100–1000 sessions/s load was demonstrated experimentally. The computational estimate for typical model parameters is ≈5152 operations for a forward pass and ≈38,944 operations, taking into account batch updating. At the same time, the main bottleneck, the O(m³) term in the Kalman step, was identified. The obtained results’ practical significance lies in the possibility of the developed adaptive neural network platform integrating into cyber police units (integration with Kafka, Spark, or Flink; exporting incidents to SIEM or SOAR; monitoring via Prometheus or Grafana) and in proposing applied optimisation paths for embedded and high-load systems. Full article

Addressing the prevalent challenges of inadequate detection accuracy and sluggish detection speed encountered by police patrol robots during security patrols, we propose an innovative algorithm based on the YOLOv8 model. Our method consists of substituting the backbone network of YOLOv8 with FasterNet. As a result, the model’s ability to identify accurately is enhanced, and its computational performance is improved. Additionally, the extraction of geographical data becomes more efficient. In addition, we introduce the BiFormer attention mechanism, incorporating dynamic sparse attention to significantly improve algorithm performance and computational efficiency. Furthermore, to bolster the regression performance of bounding boxes and enhance detection robustness, we utilize Wise-IoU as the loss function. Through experimentation across three perilous police scenarios—fighting, knife threats, and gun incidents—we demonstrate the efficacy of our proposed algorithm. The results indicate notable improvements over the original model, with enhancements of 2.42% and 5.83% in detection accuracy and speed for behavioral recognition of fighting, 2.87% and 4.67% for knife threat detection, and 3.01% and 4.91% for gun-related situation detection, respectively. Full article

The use of algorithmic models or systems in criminal justice is increasing day by day, yet the bias in these algorithms can perpetuate historical inequities, especially in predictive tools like COMPAS. This literature survey examines 30 studies addressing algorithmic bias in criminal justice. Key topics include bias types, bias detection metrics or variables such as demographic parity and equalized odds, and bias mitigation techniques like re-weighting and adversarial debiasing. Challenges in achieving fair and unbiased predictions are highlighted, including ethical considerations and trade-offs or a balance between fairness and accuracy. Insights from COMPAS and similar systems underscore the need for ongoing research, proposing potential directions for policy and practice. Full article