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.

Ontology-Based Crime News Semantic Retrieval System

Every day,the media reports tons of crimes that are considered by a large number of users and accumulate on a regular basis.Crime news exists on the Internet in unstructured formats such as books,websites,documents,and journals.From such homogeneous data,it is very challenging to extract relevant information which is a time-consuming and critical task for the public and law enforcement agencies.Keyword-based Information Retrieval(IR)systems rely on statistics to retrieve results,making it difficult to obtain relevant results.They are unable to understandthe user’s query and thus facewordmismatchesdue to context changes andthe inevitable semanticsof a given word.Therefore,such datasets need to be organized in a structured configuration,with the goal of efficiently manipulating the data while respecting the semantics of the data.An ontological semantic IR systemis needed that can find the right investigative information and find important clues to solve criminal cases.The semantic system retrieves information in view of the similarity of the semantics among indexed data and user queries.In this paper,we develop anontology-based semantic IRsystemthat leverages the latest semantic technologies including resource description framework(RDF),semantic protocol and RDF query language(SPARQL),semantic web rule language(SWRL),and web ontology language(OWL).We have conducted two experiments.In the first experiment,we implemented a keyword-based textual IR systemusing Apache Lucene.In the second experiment,we implemented a semantic systemthat uses ontology to store the data and retrieve precise results with high accuracy using SPARQL queries.The keyword-based system has filtered results with 51%accuracy,while the semantic system has filtered results with 95%accuracy,leading to significant improvements in the field and opening up new horizons for researchers.

Crime Prediction Methods Based on Machine Learning:A Survey

The objective of crime prediction,one of the most important technologies in social computing,is to extract useful information from many existing criminal records to predict the next process-related crime.It can aid the police in obtaining criminal information and warn the public to be vigilant in certain areas.With the rapid growth of big data,the Internet of Things,and other technologies,as well as the increasing use of artificial intelligence in forecasting models,crime prediction models based on deep learning techniques are accelerating.Therefore,it is necessary to classify the existing crime prediction algorithms and compare in depth the attributes and conditions that play an essential role in the analysis of crime prediction algorithms.Existing crime prediction methods can be roughly divided into two categories:those based on conventional machine learning and those based on contemporary deep learning.This survey analyses the fundamental theories and procedures.The most frequently used data sets are then enumerated,and the fundamental procedures of various algorithms are also analyzed in this paper.In light of the insufficient scale of existing data in this field,the ambiguity of data types used to predict crimes,and the absence of public data sets that have a significant impact on the research of algorithm models,this survey proposes the construction of a machine learning-based big data research model to address these issues.Future researchers who will enter this field are provided with a guide to the direction of future research development.

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。

Detection and Mapping of Violent Crime Hotspots in Southwestern Nigeria

The increasing rate of insecurity in Nigeria, especially the southwest requires a paradigm shift from popular approach to crime hotspots detection. This study employed geospatial technologies to integrate spatio-temporal crime, social media and field observation data from the communities in all the six states in the southwest to develop crime hotspots that can serve as preliminary information to assist in allocating resources for crime control and prevention. Historical crime data from January 1972 to April, 2021 were compiled and updated with rigorous field survey in September, 2021. The field data were encoded, input to the SPSS 17 and analyzed using descriptive statistics and multivariate analysis. A total 936 crime locations data were geolocated and exported to ArcGIS 10.5 for spatial mapping using point map operation and further imported to e-Spatial web-based and QGIS for the generation of hotspot map using heatmap tool. The results revealed that armed robbery, assassination and cultism were more pronounced in Lagos and Ogun States. Similarly, high incidences of farmers/herdsmen conflicts are observed in Oyo and Osun States. Increasing incidences of kidnapping are common in all the south-western states but very prominent in Ondo, Lagos and Oyo States. Most of the violent crime incidents took place along the highways, with forests being their hideouts. Violent crimes are dominantly caused by high rate of unemployment while farmer/herdsmen conflicts were majorly triggered by the scarcity of grazing fields and destruction of arable crops. The conflicts have resulted in the increasing cases of rape and disruption of social group, intake of hard drugs, cult-related activities, low income and revenue generation, and displacement of farmers and infrastructural damages. The study advocates regular retraining and equipping of security agents, establishment of cattle ranch, and installation of sophisticated IP Camera at the crime hotspots to assist in real-time crime monitoring and management.

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。

Spatial Pattern and Distribution of Crime in Suleja Lga, Niger State, Nigeria

The study examines the Spatial Pattern and Distribution of Crime in Suleja LGA, Niger State, Nigeria. The study used GIS and statistical methods to analyse the pattern and distribution of crime incidence in the study area. The records of each crime incidence were geocoded. Microsoft Excel was used to collate and organise the crime entries before they were imported into the ArcGIS Pro 2.0 environment. A geodatabase was created where the spatial and aspatial data were encoded and geospatial analysis was performed. The study reveals that the crime distribution pattern is generally clustered with a Global Moran’s I index of 0.097, a Z-score of 1.87, and a P-value < 0.06. Furthermore, the study reveals that armed robbery (61), kidnapping (40), car theft (33), culpable homicide (31), rape (29), and robbery (13) cases rank the highest in crime rate. Equally, findings of the study show that Chaza, Kwamba, Madalla, Suleja central, and Gaboda are the major crime hotspot zones at 90% confidence, as analysed using the Getis-Ord Gi* (Hot spot analysis) spatial statistics tool in ArcGIS Pro 2.0. The research therefore recommends that more effort be put into fighting crime, especially in areas where there are low-security formations, as they mostly have the highest record of crimes committed. Also, the patrol units should be equipped with GPS for better surveillance and real-time tracking of criminal activities.

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。

Enhancing Cybersecurity Competency in the Kingdom of Saudi Arabia:A Fuzzy Decision-Making Approach

The Kingdom of Saudi Arabia(KSA)has achieved significant milestones in cybersecurity.KSA has maintained solid regulatorymechanisms to prevent,trace,and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage.The widespread usage of Information Technology(IT)and IT Enable Services(ITES)reinforces securitymeasures.The constantly evolving cyber threats are a topic that is generating a lot of discussion.In this league,the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region.The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online.Different competency models have been devised to address the necessary cybercrime competencies in this context.The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security.To address this research query,the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory(Fuzzy-DMTAEL),Fuzzy Analytic Hierarchy Process(F.AHP),and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy.The similarities and differences between the three methods are also discussed.This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority.The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer amoreempowered ITES-driven economy for SaudiArabia.Moreover,cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

Addressing Japan’s disposal of nuclear-contaminated water from the perspective of international human rights law

The discharge of nuclear-contaminated water containing radionuclides into the ocean by Japan will lead to its integration into the entire ecosystem through processes of circulation and biomagnification,eventually entering the human body via the food chain.This poses a substantial risk of irreversible damage to both the ecosystem and human health,a situation that will worsen with the ongoing discharge of such water.The respect and protection of human rights represent an international consensus,and safeguarding fundamental human rights is a substantial obligation that states must undertake in accordance with both international and domestic law.Since the Fukushima nuclear disaster,Japan has continuously violated its international legal obligations to protect human rights in several areas,including the resettlement of disaster victims,the reduction of nuclear radiation levels,and the handling of contaminated water.Such actions have compromised and will continue to compromise the basic human rights of not only its citizens but also those of people worldwide,including environmental rights,the right to life,development rights,and food rights.In the aftermath of the Fukushima meltdown,the public and workers involved in handling nuclear contaminants have been continually exposed to high radiation levels,endangering their rights to life,development,and health.Japan’s inadequate efforts in victim resettlement and environmental restoration have jeopardized the environmental and food rights of its citizens to live healthily and access food in an environment unaffected by nuclear radiation.The release of nuclear-contaminated water poses a risk of Japan’s nuclear pollution to the people of neighboring countries and the global population at large.The principle of human rights underpins the theory of a community with a shared future for humanity,and human rights are a crucial area of China’s active participation in United Nations affairs and global governance.By voicing concerns over Japan’s potential human rights violations globally,China demonstrates its role as a responsible major country.In response to Japan’s breach of legal obligations and human rights violations,China can adopt a reasoned and beneficial approach,including calling on the international community to hold Japan criminally accountable for crimes against humanity under the Rome Statute and advancing scholarly discussions on ecocide and crimes against the marine environment.Furthermore,China should persist in seeking advisory opinions from the International Court of Justice and strive for substantive accountability,utilizing the mechanisms of international human rights organizations to make its voice heard.

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。

Hierarchical Restriction and Reduction of Overcriminalization of Teachers’Disciplinary Behaviors

The right to education is an important part of basic human rights. To transform from a designed vision to a reality in practice, teachers’ right to discipline, as a component of the right to education, needs tangible support from the criminal law. The criminal law cannot be absent from promoting the rule of law in education. However, in practice, teachers’ disciplinary behaviors are often ex-cessively criminalized, leading to problems such as over-expanding punishment and harming the innocent and even the malaise that en-danger substantive justice such as the tarnishing of teachers’ disci-plinary right and the imbalance of teachers’ disciplinary behaviors. Such overcriminalization has its social causes and normative crux, which is the ambiguity of regulations of teachers’ disciplinary right in terms of the pre-existing law and the unclear positioning of the jus-tification of teachers’ disciplinary behaviors in terms of the criminal law. Therefore, it is necessary to carry out a dual clarification of the chaotic parts of the two laws and determine the corresponding guiding principles, and test them one by one through the hierarchical theory of crime to make the path of exculpation clear. At the level of constituent elements, the exculpation is achieved through the normative judgment of the constituent elements;At the level of illegality, the exculpation is achieved by virtue of substantive considerations of reasons such as le-gal acts, legitimate defense, and victims’ commitments;At the level of accountability, the exculpation is achieved through the value screening of the culpability paradigm. We should reverse the trend of overcrim-inalization of teachers’ disciplinary behaviors by clearing the way of exculpation.

Crime forecasting:a machine learning and computer vision approach to crime prediction and prevention

A crime is a deliberate act that can cause physical or psychological harm,as well as property damage or loss,and can lead to punishment by a state or other authority according to the severity of the crime.The number and forms of criminal activities are increasing at an alarming rate,forcing agencies to develop efficient methods to take preventive measures.In the current scenario of rapidly increasing crime,traditional crime-solving techniques are unable to deliver results,being slow paced and less efficient.Thus,if we can come up with ways to predict crime,in detail,before it occurs,or come up with a“machine”that can assist police officers,it would lift the burden of police and help in preventing crimes.To achieve this,we suggest including machine learning(ML)and computer vision algorithms and techniques.In this paper,we describe the results of certain cases where such approaches were used,and which motivated us to pursue further research in this field.The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques.The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect,prevent,and solve crimes at a much more accurate and faster rate.In summary,ML and computer vision techniques can bring about an evolution in law agencies.

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。

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.

Temporal-spatial Distribution of Various Types of Crime in the Special Wards of Tokyo

Based on the official criminal data released by the Tokyo Metropolitan Police Department in 2019,this paper discusses the temporal-spatial distribution of various types of crimes in the special wards of Tokyo.The results show that:(1)The times of high and low incidence of different types of crime differ significantly.Although vicious crime and violent crime present no obvious monthly distribution,property crime clearly differs between the first and second half of a calendar year.(2)The month before the new year sees a surge in most types of crime.(3)Vicious crime peaks in the hours between night and early morning.Violent crime and property crime correlate positively with the frequency of human interaction and peak in the morning and evening commuting hours.(4)The spatial distribution of crime resembles the concentric circles of the three rings of the special wards of Tokyo,with a central high-incidence area,a center-peripheral low-incidence area,and a marginal high-incidence area.In addition,the center sees more personal crime than the periphery,whereas property crimes show the opposite trend.(5)A spatial autocorrelation analysis shows that the special wards of Tokyo may be grouped into the“high-high”and“low-low”agglomeration modes of different types of crime,with marked differences between the various types of crime.The crime can be divided into three types:central agglomeration,double central agglomeration,and decentralized agglomeration.

Prevalence, Pattern and Factors Associated with Psychiatric Disorders among Persons Arrested for Violent Crimes in Ife/Modakeke Area, Southwestern Nigeria

Background: Association between violence and mental disorders has contributed immensely to the stigma associated with mental illness in the society;because people erroneously see mentally ill individuals as dangerous, they will not want to associate with them. Aims: To assess the prevalence and pattern of psychiatric disorders among a sample of the violent offenders and to examine any relationship between psychiatric disorders and crimes. Method: This was a two-phase cross-sectional study in three police stations in Ile-Ife/Modakeke area of Nigeria. In the first phase, we screened 400 consecutive adults arrested for violent crimes using the General Health Questionnaire—30. In the second phase, all 36 persons with probable psychopathology were then interviewed with the Present State Examination to make a definitive diagnosis. Results: The mean age of all the subjects was 29.9 years (SD ± 7.3). The male to female ratio was 11:1. Respondents were mostly single (54%);most had secondary education or less (82%) and about 60% were currently using psychoactive substances (drugs). About 8.5% of all the subjects had a diagnosable psychiatric disorder;paranoid schizophrenia was the commonest psychiatric disorder (41.2%). Mentally ill subjects were three times more likely to commit homicidal offence than non-mentally ill subjects. Conclusion: There exists a significant but weak relationship between mental illness and violent crimes.

Analysis of the Resolution of Crime Using Predictive Modeling

There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.

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。