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Currently, a significant number of pre-trained models are published online to provide services to users owing to the rapid maturation and popularization of machine learning as a service (MLaaS). Some malicious users have pre-trained models illegally to redeploy them and earn money. However, most of the current methods focus on verifying the copyright of the model rather than tracing responsibility for the suspect model. In this study, TraceGuard is proposed, the first framework based on steganography for tracing a suspect self-supervised learning (SSL) pre-trained model, to ascertain which authorized user illegally released the suspect model or if the suspect model is independent. Concretely, the framework contains an encoder and decoder pair and the SSL pre-trained model. Initially, the base pre-trained model is frozen, and the encoder and decoder are jointly learned to ensure the two modules can embed the secret key into the cover image and extract the secret key from the embedding output by the base pre-trained model. Subsequently, the base pre-trained model is fine-tuned using stego images to implement a fingerprint while the encoder and decoder are frozen. To assure the effectiveness and robustness of the fingerprint and the utility of fingerprinted pre-trained models, three alternate steps of model stealing simulations, fine-tuning for uniqueness, and fine-tuning for utility are designed. Finally, the suspect pre-trained model is traced to its user by querying stego images. Experimental results demonstrate that TraceGuard can reliably trace suspect models and is robust against common fingerprint removal attacks such as fine-tuning, pruning, and model stealing. In the future, we will further improve the robustness against model stealing attack. [ABSTRACT FROM AUTHOR]

BACKGROUND: Heat is known to affect many health outcomes, but more evidence is needed on the impact of rising temperatures on crime and/or violence. OBJECTIVES: We conducted a systematic review with meta-analysis regarding the influence of hot temperatures on crime and/or violence. METHODS: In this systematic review and meta-analysis, we evaluated the relationship between increase in temperature and crime and/or violence for studies across the world and generated overall estimates. We searched MEDLINE and Web of Science for articles from the available database start year (1946 and 1891, respectively) to 6 November 2023 and manually reviewed reference lists of identified articles. Two investigators independently reviewed the abstracts and full-text articles to identify and summarize studies that analyzed the relationship between increasing temperature and crime, violence, or both and met a priori eligibility criteria. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were used to extract information from included articles. Some study results were combined using a profile likelihood random-effects model for meta-analysis for a subset of outcomes: violent crime (assault, homicide), property crime (theft, burglary), and sexual crime (sexual assault, rape). This review is registered at PROSPERO, CRD42023417295. RESULTS: We screened 16,634 studies with 83 meeting the inclusion criteria. Higher temperatures were significantly associated with crime, violence, or both. A 10°C (18°F) increase in short-term mean temperature exposure was associated with a 9% [95% confidence interval (CI): 7%, 12%] increase in the risk of violent crime (퐼²=30.93%; eight studies). Studies had differing definitions of crime and/or violence, exposure assessment methods, and confounder assessments. DISCUSSION: Our findings summarize the evidence supporting the association between elevated temperatures, crime, and violence, particularly for violent crimes. Associations for some categories of crime and/or violence, such as property crimes, were inconsistent. Future research should employ larger spatial/temporal scales, consistent crime and violence definitions, advanced modeling strategies, and different populations and locations. [ABSTRACT FROM AUTHOR]

Federal data indicate that assaults on transit workers resulting in fatalities or hospitalizations tripled between 2008 and 2022. The data indicated a peri-pandemic surge of assault-related fatalities and hospitalizations, but assaults with less dire outcomes were not recorded. In collaboration with the Transport Workers Union, Local 100, we conducted an online survey in late 2023 through early 2024 of New York City public-facing bus and subway workers that focused on their work experiences during the 2020–2023 period of the COVID-19 pandemic. Items for this analysis on victimization included measures of physical and sexual assault/harassment, verbal harassment/intimidation, theft, and demographic characteristics (e.g., sex, race, work division). We estimated separate modified Poisson models for each of the four outcomes, yielding prevalence ratios (PRs) and 95% confidence intervals (CIs). Potential interactions between variables with strong main effects in the adjusted model were further examined using product terms. Among 1297 respondents, 89.0% reported any victimization; respondents also reported physical assault (48.6%), sexual assault/harassment (6.3%), verbal harassment/intimidation (48.7%), and theft on the transit system (20.6%). Physical assault was significantly more common among women in the bus division compared to female subway workers, male bus workers, and male subway workers (adjusted PR (aPR) = 3.54; reference = male subway workers; Wald test p <.001). With the same reference group, sexual assault/harassment was more frequently reported among female subway workers (aPR = 5.15; Wald test, p <.001), but verbal assault/intimidation and experiencing theft were least common among women in the bus division (aPR = 0.22 and 0.13, respectively; Wald tests, p <.001). These data point to the need for greater attention to record and report on victimization against workers in both buses and subway. [ABSTRACT FROM AUTHOR]

This article explores how entrepreneurial small- and medium-sized enterprises (SMEs) manage coopetition strategies to innovate with large firms. While coopetition offers opportunities for innovation and growth, asymmetries between SMEs and large firms can provoke unilateral actions, opportunistic tactics, and knowledge theft which can undermine SME innovation power and jeopardise coopetition success. Based on a qualitative multiple-case study of 25 coopetitive innovation projects, each involving an SME and a large firm, we find that SMEs manage these risks by pursuing a synergistic mix of three distinct coopetition strategies: (1) Co-distribution, (2) Technology licensing, and (3) R&D co-development. In each strategy, SMEs navigate different coopetition intensities by dynamically combining the principles of separation, integration, co-management and co-ownership to achieve specific innovation outcomes. Our findings suggest that SMEs shift between cooperation- and competition-dominant strategies and employ a mix of management principles to offset asymmetrical risks and maximise their innovation benefits from coopetition with large firms. [ABSTRACT FROM AUTHOR]

Abstract The COVID-19 pandemic has significantly impacted crime rates in Kerman province, Iran, revealing divergent patterns compared to global trends. This ecological study utilized the Poisson regression model and interrupted time-series Poisson regression model to analyze data collected from the Iranian Legal Medicine Organization (ILMO) database and the Kerman Criminal Investigation Department of Police database, focusing on theft, conflicts, and homicide rates in this region. Our findings indicate that in 2020, the incidence rate ratio of conflicts increased compared to previous years. Before the pandemic, thefts were on the rise, while homicides were decreasing. However, with the onset of the pandemic, thefts initially decreased, and homicides experienced a notable increase. Subsequently, both thefts and homicides showed a declining trend. By the end of 2020, thefts were significantly lower than expected based on pre-pandemic data, while homicides were slightly higher. Initial fluctuations in crime rates may be related to quarantine measures and the challenges faced during the pandemic, while subsequent changes might be influenced by governmental interventions. Understanding the complex relationship between public health emergencies and criminal activities is crucial for informing policymaking and crime prevention efforts.

Firearm-related interpersonal violence is a leading cause of death and injury in cities across the United States, and understanding the movement of firearms from on-the-books sales to criminal end-user is critical to the formulation of gun violence prevention policy. In this study, we assemble a unique dataset that combines records for over 380,000 crime guns recovered by law enforcement in California (2010-2021), and more than 126,000 guns reported stolen, linked to in-state legal handgun transactions (1996-2021), to describe local and statewide crime gun trends and investigate several potentially important sources of guns to criminals, including privately manufactured firearms (PMFs), theft, and dirty dealers. We document a dramatic increase over the decade in firearms recovered shortly after purchase (7% were recovered within a year in 2010, up to 33% in 2021). This corresponds with a substantial rise in handgun purchasing over the decade, suggesting some fraction of newly and legally acquired firearms are likely diverted from the legal market for criminal use. We document the rapid growth of PMFs over the past 2-3 years and find theft plays some, though possibly diminishing, role as a crime gun source. Finally, we find evidence that some retailers contribute disproportionately to the supply of crime guns, though there appear to be fewer problematic dealers now than there were a decade ago. Overall, our study points to temporal shifts in the dynamics of criminal firearms commerce as well as significant city variation in the channels by which criminals acquire crime guns.

Based on the analysis of the preserved materials of the judicial and investigative cases of Munster in the first half of the XVII century, the article identifies the features of the women’s crime in the city. It is shown that the main types of women’s crimes were witchcraft, infanticide, thefts. The state of the women’s crime was influenced by socio-demographic factors, as well as the lack of financial assistance and the social support from Munster’s authorities. The conclusion is made about the insignificant number of crimes committed by women in the first half of the XVII century compared to men.

Background: Social vulnerability may play a role in social media-involved crime, but few studies have investigated this issue. We investigated associations between social vulnerability and social media-involved violent crimes. Methods: We analyzed 22,801 violent crimes occurring between 2018 and 2023 in Prince George's County, Maryland. Social media involvement was obtained from crime reports at the Prince George's County Police Department. Social media application types included social networking, advertising/selling, ridesharing, dating, image/video hosting, mobile payment, instant messaging/Voice over Internet Protocol, and other. We used the Centers for Disease Control and Prevention's Social Vulnerability Index to assess socioeconomic status (SES), household characteristics, racial and ethnic minority status, housing type and transportation, and overall vulnerability. Modified Poisson models estimated adjusted prevalence ratios (aPRs) among the overall sample and stratified by crime type (assault and homicide, robbery, and sexual offense). Covariates included year and crime type. Results: Relative to high tertile areas, we observed a higher prevalence of social media-involved violent crimes in areas with low SES vulnerability (aPR: 1.82, 95% CI: 1.37-2.43), low housing type and transportation vulnerability (aPR: 1.53, 95% CI: 1.17-2.02), and low overall vulnerability (aPR: 1.63, 95% CI: 1.23-2.17). Low SES vulnerability areas were significantly associated with higher prevalences of social media-involved assaults and homicides (aPR: 1.64, 95% CI: 1.02-2.62), robberies (aPR: 2.00, 95% CI: 1.28-3.12), and sexual offenses (aPR: 2.07, 95% CI: 1.02-4.19) compared to high SES vulnerability areas. Low housing type and transportation vulnerability (vs. high) was significantly associated with a higher prevalence of social media-involved robberies (aPR: 1.54, 95% CI:1.01-2.37). Modified Poisson models also indicated that low overall vulnerability areas had higher prevalences of social media-involved robberies (aPR: 1.71, 95% CI: 1.10-2.67) and sexual offenses (aPR: 2.14, 95% CI: 1.05-4.39) than high overall vulnerability areas. Conclusions: We quantified the prevalence of social media-involved violent crimes across social vulnerability levels. These insights underscore the need for collecting incident-based social media involvement in crime reports among law enforcement agencies across the United States and internationally. Comprehensive data collection at the national and international levels provides the capacity to elucidate the relationships between neighborhoods, social media, and population health. [ABSTRACT FROM AUTHOR]

Wild birds have experienced unprecedented, near‐global mass mortalities since 2021, driven by outbreaks of high‐pathogenicity avian influenza virus (HPAIV) H5N1 lineage 2.3.4.4b. Managing this panzootic requires identification of transmission pathways. We investigated potential HPAIV transmission via kleptoparasitism (food theft) by examining the distribution, behaviors, and movements of two globally widespread and commonly kleptoparasitic seabird families: Fregatidae (frigatebirds) and Stercorariidae (skuas). These kleptoparasites force other seabirds (targets) to regurgitate food, which the kleptoparasite then ingests, potentially facilitating direct transfer of viral particles from target to kleptoparasite. Scavenging and predation probably contribute further to viral spread. Although frigatebirds use kleptoparasitism on a year‐round basis, skuas more commonly do so outside of the breeding season. Both frequently forage, disperse, or migrate across oceans and hemispheres. Dense aggregations of kleptoparasitic and target seabirds at breeding and/or roosting sites may facilitate the spread of HPAIV. In addition, the migration of these species could also facilitate broadscale geographic spread of HPAIV. Surveillance of kleptoparasites for HPAIVs could aid in early detection and may be important for seabird conservation. [ABSTRACT FROM AUTHOR]

Secure instant communication is an important topic of information security. A group chat is a highly convenient mode of instant communication. Increasingly, companies are adopting group chats as a daily office communication tool. However, a large volume of messages in group chat communication can lead to message overload, causing group members to miss important information. Additionally, the communication operator's server may engage in the unreliable behavior of stealing information from the group chat. To address these issues, this paper proposes an attribute-based end-to-end policy-controlled signcryption scheme, aimed at establishing a secure and user-friendly group chat communication mode. By using the linear secret sharing scheme (LSSS) with strong expressive power to construct the access structure in the signcryption technology, the sender can precisely control the recipients of the group chat information to avoid message overload. To minimize computational cost, a signcryption step with constant computational overhead is designed. Additionally, a message-sending mechanism combining "signcryption + encryption" is employed to prevent the operator server from maliciously stealing group chat information. Rigorous analysis shows that PCE-EtoE can resist adaptive chosen-ciphertext attacks under the standard model. Simulation results demonstrate that our theoretical derivation is correct, and that the PCE-EtoE scheme outperforms existing schemes in terms of computational cost, making it suitable for group chat communication. [ABSTRACT FROM AUTHOR]

The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation. [ABSTRACT FROM AUTHOR]

Background: In recent years, there has been a growing number of thefts of firearms stored in vehicles. Despite this trend, there is limited research on firearm storage patterns in vehicles in the United States. This study investigates these storage patterns and evaluates the relationship between the surge in firearm purchases after March 2020 and the practice of storing firearms in vehicles. Methods: Firearm storage practices were classified into four categories: (a) no vehicle storage, (b) locked vehicle storage only, (c) unlocked vehicle storage only, and (d) both locked and unlocked vehicle storage. Multinomial logistic regression analyses were conducted to determine the association between vehicle firearm storage practices and the main independent variable (firearm purchases since March 2020), adjusting for covariates. Results: Those who purchased a firearm since March 2020 were significantly more likely to store at least one firearm in a vehicle unlocked only (RRR = 2.41, 95% CI 1.45–3.99) or both locked and unlocked (RRR = 2.57, 95% CI.180–3.67) compared to the reference category of no vehicle storage. Conclusion: Individuals who purchased a firearm after March 2020 were more likely to report storing a firearm in a vehicle. Given the limited research on patterns of firearm storage in vehicles, these findings provide novel evidence suggesting that firearm purchases following the March 2020 firearm purchasing surge may have fomented behaviors that increased the likelihood of firearm storage in automobiles. Moving forward, there is a need for additional quantitative and qualitative research that can better understand patterns and motivations of firearm storage in vehicles. [ABSTRACT FROM AUTHOR]

This study assesses the theft and mutilation of library resources at Walton Whaley Library (WWL) at Valley View University, in Ghana. The study adopted a mixed-method approach; thus, copies of questionnaire and interviews were used to gather data for the study. The quantitative and qualitative data were analyzed with the help of Nvivo version 11 and IBM Statistical Package for the Social Sciences (IBM-SPSS version 21.0). The study found that most of the users were aware of the security policies in the library. The prominent factors influencing theft and mutilation are a weak security system, the absence of closed-circuit television (CCTV) cameras, inadequate staff, the selfish interests of the perpetrators, and a lack of security personnel at the main entrance. To curb this phenomenon, certain security measures, such as the installation of electronic security devices, security personnel at the main entrance, physical body checks on users entering and exiting the Library, as well as staff surveillance are needed. The study recommended an effective and efficient security system, the vigilance of library staff, the acquisition of multiple books in high demand, availability and accessibility of photocopying services. The study suggested harsh punishment for perpetrators who breach library rules. [ABSTRACT FROM AUTHOR]

The purpose of this article is a scientific analysis of the methodological foundations of the criminalistics characterization of thefts of cultural values from religious buildings. It is shown that the criminalistics characteristics of theft of cultural values from religious buildings is a group (special) characteristic. It, in addition to the forensic signs of theft, must contain special signs relating to the object of the direct attempt - cultural values, as well as their location at the time of the theft - a religious building. It was concluded that the criminalistics characteristics of theft of cultural values from religious buildings can be presented in the form of descriptions, tables, diagrams, graphs, computer programs, typologies, etc. Such a characteristic should be based on probabilistic-statistical and correlational relationships between its elements. In the author's opinion, for the investigation of thefts of cultural values from places of worship, the most productive presentation will be the presentation of criminalistics characteristics in the form of a description, typology and analysis of the phases of criminal activity, which will make it possible to most fully describe and present the most essential features of the elements of the criminalistic characteristics of thefts of cultural values from places of worship. It is shown that the functions of criminalistics characterization of criminal offenses are more multifaceted than the simple focus on putting forward versions of a criminal offense committed in conditions of non-obviousness. According to the author, critics of the «impracticality» of forensic characterization of criminal offenses need to look at the functions that such characterization performs in criminalistics in general and forensic methodology, in particular. Such functions have already been highlighted, they are: applied (typical signs of elements and connections between them can be used during the disclosure and investigation of criminal offenses); cognitive (information about the signs of a criminal offense helps the subject of its knowledge - employees of the operational unit, the investigator to learn about the object, that is, the criminal offense); substitution (forensic characteristics can be considered as an information model that replaces the «original», helping to know it); informative (criminalistics characteristics not only reflect the original information about the object of knowledge, but also help to obtain new information about it); prognostic (information contained in criminalistics characteristics helps to make predictions about a possible series of crimes, about the circumstances that may accompany them, etc.); organizational and methodical (criminalistics characteristics guide the investigator to choose the most appropriate means of investigating a crime, to the optimal organization of such an investigation). [ABSTRACT FROM AUTHOR]

Membership inference attack opens up a newly emerging and rapidly growing research to steal user privacy from text classification models, a core problem of which is shadow model construction and members distribution optimization in inadequate members. The textual semantic is likely disrupted by simple text augmentation techniques, which weakens the correlation between labels and texts and reduces the precision of member classification. Shadow models trained exclusively with cross-entropy loss have little differentiation in embeddings among various classes, which deviates from the distribution of target models, then impacts the embeddings of members and reduces the F1 score. A competitive and High-Availability Membership Inference Attack against Text Classification Model (HAMIATCM) is proposed. At the data level, by selecting highly significant words and applying text augmentation techniques such as replacement or deletion, we expand knowledge of attackers, preserving vulnerable members to enhance the sensitive member distribution. At the model level, constructing contrastive loss and adaptive boundary loss to amplify the distribution differences among various classes, dynamically optimize the boundaries of members, enhancing the text representation capability of the shadow model and the classification performance of the attack classifier. Experimental results demonstrate that HAMIATCM achieves new state-of-the-art, significantly reduces the false positive rate, and strengthens the capability of fitting the output distribution of the target model with less knowledge of members. [ABSTRACT FROM AUTHOR]

The detection of Android malware is of paramount importance for safeguarding users' personal and financial data from theft and misuse. It plays a critical role in ensuring the security and privacy of sensitive information on mobile devices, thereby preventing unauthorized access and potential damage. Moreover, effective malware detection is essential for maintaining device performance and reliability by mitigating the risks posed by malicious software. This paper introduces a novel approach to Android malware detection, leveraging a publicly available dataset in conjunction with a Genetic Programming Symbolic Classifier (GPSC). The primary objective is to generate symbolic expressions (SEs) that can accurately identify malware with high precision. To address the challenge of imbalanced class distribution within the dataset, various oversampling techniques are employed. Optimal hyperparameter configurations for GPSC are determined through a random hyperparameter values search (RHVS) method developed in this research. The GPSC model is trained using a 10-fold cross-validation (10FCV) technique, producing a set of 10 SEs for each dataset variation. Subsequently, the most effective SEs are integrated into a threshold-based voting ensemble (TBVE) system, which is then evaluated on the original dataset. The proposed methodology achieves a maximum accuracy of 0.956, thereby demonstrating its effectiveness for Android malware detection. [ABSTRACT FROM AUTHOR]

Background: Adverse Childhood Experiences (ACEs) are linked to the development of anxiety in adolescence. According to recent studies, the COVID-19 pandemic represents a novel ACE that is associated with anxiety among adolescents. This study investigates the relationship between ACEs, COVID-19, and anxiety in adolescents. Methods: A cross-sectional study was conducted using a community sample of 248 boys and girls ages 12 to 15 years (mean = 13.50 years) from five high schools in Eastern Attica. A total of four questionnaires were used: (1) Demographic Questionnaire, (2) State–Trait Anxiety Inventory for Children—STAIC, (3) Adverse Childhood Experiences Scale, and (4) COVID-19 Impact Scale. Results: The results demonstrated a moderate association between the total number of ACEs and anxiety (trait and state) in adolescence (trait anxiety: ρ = 0.37, p < 0.001; state anxiety: ρ = 0.29, p < 0.001). Girls scored significantly higher than boys on both trait anxiety (U = 4353, p < 0.001; mean difference = 5.5) and state anxiety (U = 5822.5, p = 0.014; mean difference = 2). The number of ACEs was found to be significantly related to the impact of COVID-19 (β = 0.025, p < 0.001). Conclusions: This study highlights the significant link between ACEs and increased anxiety in adolescents, which is further exacerbated by the COVID-19 pandemic. The findings indicate that girls are more affected than boys. These results emphasize the need for targeted mental health interventions to enhance coping mechanisms, reduce stress, and address anxiety in adolescents, particularly during global crises like the pandemic. Developing such programs is essential for supporting the mental well-being of youth facing multiple stressors. [ABSTRACT FROM AUTHOR]

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized. [ABSTRACT FROM AUTHOR]

This research is about "The crime of assaulting money transport vehicles, and its impact on proving Limitation or discretion", through the descriptive, inductive, analytical, comparative, and deductive research methodology. The topic is considered one of the jurisprudential developments that this study dealt with, and it included two main aspects: First: Establishing the issue of the truth about the registration of money transport vehicles, depending on their circumstances. Second: Listing the forms of the crime of assaulting money transport vehicles and explaining its ruling according to its jurisprudential description in terms of proving the punishment for theft, or the punishment for banditry, or considering the discretionary punishment when the conditions for the obligation to limit theft as theft or banditry are not met. [ABSTRACT FROM AUTHOR]

Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study's contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions. [ABSTRACT FROM AUTHOR]

Reparations proposals typically target wealth. Yet slavery’s and Jim Crow’s long echoes also steal time, such as by producing shorter Black lifespans even today. I argue that lost time should be considered an independent target for redress; identify challenges to doing so; and provide examples of what reparations redressing lost lifespan could look like. To identify quantitative targets for redress, I analyze area-level relationships between Black lifespans and six measures of intensity of slavery, Jim Crow, and racial terror. Results reveal inconsistent relationships across measures, suggesting difficulties in grounding a target for redress in such variation. Instead, I propose that policies aim to redress the national lifespan gap between White and Black Americans. The article concludes with a typology of potential strategies for such redress. [ABSTRACT FROM AUTHOR]

Smart power grids suffer from electricity theft cyber-attacks, where malicious consumers compromise their smart meters (SMs) to downscale the reported electricity consumption readings. This problem costs electric utility companies worldwide considerable financial burdens and threatens power grid stability. Therefore, several machine learning (ML)-based solutions have been proposed to detect electricity theft; however, they have limitations. First, most existing works employ supervised learning that requires the availability of labeled datasets of benign and malicious electricity usage samples. Unfortunately, this approach is not practical due to the scarcity of real malicious electricity usage samples. Moreover, training a supervised detector on specific cyberattack scenarios results in a robust detector against those attacks, but it might fail to detect new attack scenarios. Second, although a few works investigated anomaly detectors for electricity theft, none of the existing works addressed consumers' privacy. To address these limitations, in this paper, we propose a comprehensive federated learning (FL)-based deep anomaly detection framework tailored for practical, reliable, and privacy-preserving energy theft detection. In our proposed framework, consumers train local deep autoencoder-based detectors on their private electricity usage data and only share their trained detectors' parameters with an EUC aggregation server to iteratively build a global anomaly detector. Our extensive experimental results not only demonstrate the superior performance of our anomaly detector compared to the supervised detectors but also the capability of our proposed FL-based anomaly detector to accurately detect zero-day attacks of electricity theft while preserving consumers' privacy. [ABSTRACT FROM AUTHOR]

Based on the concepts of cultural memory and binary culture, as proposed by the cultural semiotician Iuri Lotman, this article discusses the ideia of cultural appropriation through the attempt of transforming acarajé, as a symbol and cultural text, into Jesus fritter (bolinho de Jesus). For this purpose, it intends to elucidate how the cultural appropriation process implies on a dispute for memory. Therefore, it is discussed how certain religious segments related to evangelical Christianity or Brazilian neocharismatic aims to impose the erasing of the cultural text memory by stealing one sign over another. As a result, this study identified how the semiotic mechanism works in order to simulate an alleged emptying of history and the meaning to a sign to try to forge another religious guidance to it. [ABSTRACT FROM AUTHOR]

Antiquity theft occurs all around the world. However, there is no consensus on how to stop it. Do source States strive to crack down on looters? Do States cooperate to stop the transit of antiquities? Or do we ask receiving States to punish the buyers? Given the difficulty of stopping looters and penalizing facilitators, the most effective route seems to be punishing the buyers. However, while the United States has enacted laws that are used to combat cultural property theft—the Convention on Cultural Property Implementation Act, the National Stolen Property Act, and customs laws—prosecutors have been hesitant to criminally charge buyers due to these laws’ demanding mens rea requirements. This Note argues that customs laws could become potent weapons against the theft of cultural property if the level of moral culpability required is reduced. [ABSTRACT FROM AUTHOR]

Key Clinical Message: Splenic steal syndrome (SASS) represents a challenge to interventional radiologists after orthotopic liver transplantation. In this case series, we present three cases of patients who developed SASS after their liver transplants. [ABSTRACT FROM AUTHOR]

In discussing truth in Being and Time Heidegger speaks of this as always involving a robbery (ein Raub). This is a revealing word but not noted by commentators. Is it an incidental metaphor not to be taken too seriously? Or does it help us focus on something of the informing orientation to being and truth marking the Heidegger of Being and Time? This reflection offers thoughts on Alētheia in light of the meaning of theft. Significantly, the later Heidegger witnesses an ontological attunement entirely other to that of a robber who steals what is not his own. This attunement is closer to one of being graced or gifted by being rather than stealing secrets from the hiddenness of being. What terms do we need to make sense of theft and gift; how do we turn from, get from, theft to gift? How get from Polemos to Gelassenheit? Does Heidegger leave us in the dark on this great question? [ABSTRACT FROM AUTHOR]

Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identified through anomalous consumption patterns observed in smart networks. However, a smart model is required for capturing and analysing the data intelligently to accurately detect electricity theft. In the paper, electricity theft has been detected using an encoder-decoder-based classifier that integrates two models of convolutional neural networks (CNN). The aim is to scan the strength of the data and built a smart model that analysed the connections in complex data and determine the pattern of theft. The model comprises three compartments: the auto-encoder, the wide convolutional neural network (1-D CNN model), and the deep convolutional neural network (2-D CNN model). The auto-encoder has been trained on the complex and in-depth linkage between the theft data and the normal data as it removes noise and unnecessary information. The 1-D CNN model gathers relevant connections and general features, while the 2-D CNN model determines the rate at which energy theft occurs and differentiates between the energy-stealing consumers and normal consumers. The efficacy of the approach is underscored by its superiority over traditional deep learning and machine learning techniques. This paper elucidates the distinct advantages and applications of the proposed model in combating electricity theft within smart grid environments. [ABSTRACT FROM AUTHOR]

Electricity theft (ET), which endangers public safety, interferes with the regular operation of grid infrastructure, and increases revenue losses, is a significant issue for power companies. To find ET, numerous machine learning, deep learning, and mathematically based algorithms have been published in the literature. However, these models do not yield the greatest results due to issues like the dimensionality curse, class imbalance, inappropriate hyper‐parameter tuning of machine learning, deep learning models etc. A hybrid DL model is presented for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre‐processing techniques are first employed to clean up the data from the smart meters, and then the feature extraction technique, AlexNet is used to address the curse of dimensionality. An actual dataset of Chinese smart meters is used in simulations to assess the efficacy of the suggested approach. To conduct a comparative analysis, various benchmark models are implemented as well. This proposed model achieves accuracy, precision, recall, and F1‐score, up to 86%, 89%, 86%, and 84%, respectively. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

Time theft, especially with the shift to remote work during the pandemic, is an increasing challenge for organizations. Existing studies demonstrate that both authoritarian leadership and laissez-faire leadership can exacerbate time theft, putting leaders in a behavioral dilemma of neither being strict nor lenient. Additionally, the pervasive and covert nature of time theft diminishes the effectiveness of subsequent corrective actions. Our study aims to investigate how to prevent time theft by mitigating employees' inclinations. Based on role theory, our study examines whether supervisor developmental feedback can encourage employees to perform work roles more appropriately. To uncover the complicated internalization process of role expectation, our study incorporates perceived insider status and work passion as serial mediators and considers the boundary effect of leaders' word–deed consistency. In Study 1, a survey of 402 employees revealed that supervisor developmental feedback can negatively predict employee time theft through employees' perceived insider status and work passion. Study 2 employs the same sample to further identify three topics of supervisor developmental feedback: skill learning, attitude learning, and social learning. Moreover, serial multiple mediating effects are affirmed across topics. The findings suggest that providing feedback on employees' learning and growth is an effective approach to prevent time theft. [ABSTRACT FROM AUTHOR]

We take into account a few combinatorial issues brought on by the requirement to provide security on a communications network. One of the key factors in the design of a security system is key management. We go through key distribution patterns, a way to condense the number of keys stored in a big network, and secret sharing systems that can be used to safeguard keys from theft or unauthorized access. We outline some combinatorial patterns with cordiality, referred to as authentication schemes, that could be utilized to create encodings that can spot such changes. [ABSTRACT FROM AUTHOR]