基于CRIME理论的康美药业财务造假分析及防范研究

随着经济的快速发展,资本市场中财务造假事件频繁发生,损害了中小股东的利益,给债权人、广大投资者乃至社会带来了巨大的损失。利用传统财务指标对舞弊手段进行识别的作用有限,提出的解决措施治标不治本。因此,需要采用系统的理论对上市公司财务舞弊进行深入分析,才能提出针对性的治理对策。本文运用CRIME舞弊行为理论,从舞弊者、舞弊手段、舞弊动机、监管机制、舞弊后果五个角度,对康美药业舞弊现象进行剖析,针对产生的问题提出了相应的预防措施,以期增强投资者的风险辨识能力。

基于CRIME理论的^(*)ST宜生财务舞弊案例研究

资本市场不断完善对我国经济发展起到重要的推动作用,但同时财务舞弊事件也层出不穷。上市公司财务舞弊行为严重侵害了投资者权益,扰乱了我国资本市场的正常运行,不利于社会经济健康发展。运用CRIME理论,分别从造假行为人、造假手段、造假动因、监督机制、造假结果5个方面对^(*)ST宜生财务舞弊事件进行分析,厘清了财务舞弊的来龙去脉,并针对性地从公司、会计师事务所和监管机制3个角度提供启示,希望对相关主体具有借鉴意义,减少财务舞弊行为的发生,肃清资本市场不良风气。

从CRIME理论看公司财务造假

随着经济的飞速发展,我国上市公司数量逐年增加,但并不是所有的上市公司都能够保持持续盈利或者在行业中占据主导地位,财务造假严重影响着正常的经济运作规律,给社会带来巨大的危害。本文从CRIME理论五个因素出发,以中信国安公司财务造假为案例,对该公司造假者、造假手段、造假动机、监管机制、最终后果进行探究,从中寻找启示。

Terrorism Attack Classification Using Machine Learning: The Effectiveness of Using Textual Features Extracted from GTD Dataset

One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.

A Survey of Crime Scene Investigation Image Retrieval Using Deep Learning

Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.

The Windy City’s Dark Side: A Statistical Exploration of Crime in the City of Chicago

This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.

From Ideology to Action:Analyzing Rapoport’s Terrorism Framework in the Context of Al-Shabaab’s Evolving Extremist Ideology in the Horn of Africa

This study examines the evolution of Al-Shabaab’s extremist ideology in the Horn of Africa through David Rapoport’s Four Waves of Terrorism framework. It aims to analyze the persistent influence of Al-Shabaab amidst counterinsurgency efforts, highlighting the interplay between local grievances and global jihadist narratives. Through an ideological analysis of secondary sources, the research reveals that Al-Shabaab merges Somali nationalism with global jihadist ideologies, framing its struggle as both a local defense against foreign intervention and part of Islamic movement. This dual narrative is crucial for sustaining recruitment and operational resilience, illustrating the complexities of contemporary terrorism in the region.

基于CRIME理论视角下东方金钰财务舞弊分析

近年来,随着资本市场的不断完善,我国经济得到了高速发展,但与此同时伴随着上市公司财务舞弊行为的频繁发生,这不仅极大损害了投资者的利益,还严重影响了资本市场的诚信机制。因此,对上市公司财务舞弊行为进行有效识别与采取防范手段极为重要。本文以东方金钰财务造假案件为例,运用CRIME理论,分别从舞弊行为人、舞弊手段、舞弊动因、监管机制、舞弊结果五个方面对东方金钰财务舞弊事件进行分析,并针对这五个方面提出有关的建议。希望对相关研究具有借鉴意义,以减少财务舞弊行为的发生。

基于CRIME理论的财务舞弊问题研究——以Z公司为例

随着数字经济和互联网的兴起,上市公司财务舞弊现象层出不穷。本文基于CRIME理论,从财务的舞弊者、舞弊手段、舞弊动机、舞弊监管机制、舞弊结果五个方面系统分析了Z公司的财务舞弊案例,从内部防范、人员素质、外部监管三个方面提出了建议,希望对资本市场的完善有所启示。

通过扫黑除恶的社会治理——以贯彻落实《反有组织犯罪法》为视角

在黑恶势力及其违法犯罪严重影响基层政权稳定的严峻形势下,党和国家决定开展扫黑除恶专项斗争,经过三年的全面、深入、系统推进,成效显著,赢得全面胜利。扫黑除恶是国家专门力量直接介入基层社会治理的重要体现,但单纯通过刑事司法或综合治理均无法达到深入且快速的良好治理效果。在总体国家安全观的指导下《,反有组织犯罪法》以“四个结合”为治理理念,构建惩防并举、标本兼治的有组织犯罪治理体系。其具体体现了司法治理与综合治理并重,且重点突出、有机衔接的共治路径及其立法思路。其中,司法治理强调宽严相济刑事政策的细化落实及关联治理机制的体系性完备,综合治理则突出群众路线下治本且治标的综合预防体系健全。通过不断完善的法治来推动常态化、高质效的有组织犯罪治理,保障扫黑除恶的法律效果、政治效果与社会效果相统一,有利于促进实现国家治理体系和治理能力现代化。

侵犯商业秘密罪评价标准的修正与规范解读

《刑法修正案(十一)》完成了侵犯商业秘密罪由数额犯到情节犯的全面转型,但未跟进更新的司法解释易使得司法实务将情节犯作数额犯化处理,仅关注数额要素对于犯罪评价的影响。侵犯商业秘密罪所保护的是复合法益:一是商业秘密权利人对商业秘密享有的私权利,二是市场竞争秩序,后者又可还原为具体的个人法益——商业秘密权。以此为逻辑起点,侵犯商业秘密罪的评价体系应当坚持区分性、形式与实质相统一、强保护的原则,建构综合评价体系。各要素不可单独作为入罪标准,而应综合考量,建构起以数额要素为主,非数额要素为辅的“情节严重”解释体系。

hate crime译名商榷

hate crime是警务英语中使用较多的一个术语,但其译名并不统一。通过追溯该术语的概念和内涵,分析比较既有译名,找出翻译过程中法律文化缺省和翻译方法不当的问题,笔者结合中外法律文化特点,采用仿译的方法,提出新的译名。

骗取贷款罪中“重大损失”之理解

虽然《中华人民共和国刑法修正案(十一)》已经修改骗取贷款罪的入罪条件,然而关于重大损失的数额计算、产生时间和体系位置等问题的学理讨论尚不充分。重大损失不应包括放贷利息,其产生于贷款到期之后。将重大损失理解成骗取贷款罪客观处罚条件的观点,不符合立法修改主旨和刑法条文内在逻辑,缺乏刑事政策上的合理性。客观超过要素理论本身存在缺陷,其也不适合描述重大损失在骗取贷款罪中的体系位置。骗取贷款罪中“重大损失”与“特别重大损失”直接影响行为刑事可罚性,属于骗取贷款罪的构成要件要素。

Disaster preparedness, pediatric considerations in primary blast injury, chemical, and biological terrorism

Both domestic and foreign terror incidents are an unfortunate outgrowth of our modern times from the Oklahoma City bombings, Sarin gas attacks in Japan, the Madrid train bombing, anthrax spores in the mail, to the World Trade Center on September 11 th, 2001. The modalities used to perpetrate these terrorist acts range from conventional weapons to high explosives, chemical weapons, and biological weapons all of which have been used in the recent past. While these weapons platforms can cause significant injury requiring critical care the mechanism of injury, pathophysiology and treatment of these injuries are unfamiliar to many critical care providers. Additionally the pediatric population is particularly vulnerable to these types of attacks. In the event of a mass casualty incident both adult and pediatric critical care practitioners will likely be called upon to care for children and adults alike. We will review the presentation, pathophysiology, and treatment of victims of blast injury, chemical weapons, and biological weapons. The focus will be on those injuries not commonly encountered in critical care practice, primary blast injuries, category A pathogens likely to be used in terrorist incidents, and chemical weapons including nerve agents, vesicants, pulmonary agents, cyanide, and riot control agents with special attention paid to pediatric specific considerations.

基于CRIME五因素论的ST公司财务舞弊识别

ST公司财务舞弊对我国资本市场造成了不小的负面影响。本文以舞弊动因"CRIME"五因素论为基石,选取可能影响舞弊的22个指标,并使用3年的数据对30对ST公司舞弊与非舞弊样本做了t检验、Wilcoxon符号秩检验、Logistic回归,建立的识别模型综合正确率达到81.7%。通过对模型进行稳健性检验,结果表明,识别舞弊样本的正确率为77.3%,说明该模型具有良好的应用效果。

Monitoring and Early Warning of New Cyber-Telecom Crime Platform Based on BERT Migration Learning

The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.

Predicting the Type of Crime: Intelligence Gathering and Crime Analysis

Crimes are expected to rise with an increase in population and the rising gap between society’s income levels.Crimes contribute to a significant portion of the socioeconomic loss to any society,not only through its indirect damage to the social fabric and peace but also the more direct negative impacts on the economy,social parameters,and reputation of a nation.Policing and other preventive resources are limited and have to be utilized.The conventional methods are being superseded by more modern approaches of machine learning algorithms capable of making predictions where the relationships between the features and the outcomes are complex.Making it possible for such algorithms to provide indicators of specific areas that may become criminal hot-spots.These predictions can be used by policymakers and police personals alike to make effective and informed strategies that can curtail criminal activities and contribute to the nation’s development.This paper aims to predict factors that most affected crimes in Saudi Arabia by developing a machine learning model to predict an acceptable output value.Our results show that FAMD as features selection methods showed more accuracy on machine learning classifiers than the PCA method.The naïve Bayes classifier performs better than other classifiers on both features selections methods with an accuracy of 97.53%for FAMD,and PCA equals to 97.10%.

瑞幸咖啡财务舞弊事件分析——基于CRIME模型

移动互联网时代给了企业前所未有的创新和实践的机会,但伴随的是诸多的不确定性,商业模式的创新给企业带来全新盈利模式的同时也隐藏着巨大风险。瑞幸造假事件给我们思考资本逐利本性、监管的缺位提供了一个很好的样本。文章通过财务舞弊理论——CRIME模型,从舞弊行为人、舞弊手段、舞弊动机、监管机制以及结果五个方面来对瑞幸财务舞弊事件进行了全面分析,认为瑞幸造假事件的发生除了创始人陆正耀"一股独大"造成的公司内部管理失衡外,还在于中介机构和政府层面的监管存在疏漏,因此,要借此次瑞幸造假事件,反思自身的不足,加大企业欺诈的犯罪成本。

基于CRIME理论的财务舞弊案例分析——以康美药业为例

康美药业财务舞弊案作为A股史上最大的财务舞弊案,一直备受市场关注。文章基于CRIME理论,从舞弊行为人、舞弊手段、舞弊激励、监管机制、结果五个方面系统全面地分析了康美药业财务舞弊案,并从内部监管与外部监管两方面提出建议。

The dynamics of poverty and crime

Poverty and crime are two maladies that plague metropolitan areas. The economic theory of crime[1]demonstrates a direct correlation between poverty and crime. The model considered in this study seeks to examine the dynamics of the poverty-crime system through stability analysis of a system of ordinary differential equations in order to identify cost-effective strategies to combat crime in metropolises.