日志
2012年北美黑龙江大学获奖论文
|
北美论文到底难不难呢？Locating the conspirators in the company
The mathematical model built in this paper can analyze the complex crime reasonably. According
to a part of known arrested suspects and their social relationship, the model can discover the
conspirator with the maximum possibility, which can accelerate the process of investigating cases
With the the suspicious information, known conspirators and innocents, a model of finding
internal crime suspects in the company is established. For a person in the company, if he has
closer contact with other members he will has more possibility to commit a crime, which is called
core degree. Making use of Matrix replacement method and Hierarchical clustering method, we
can find the group of higher core degree and sort them with descending. Next, we deem that a
member who has closer contact with a conspirator will be more possibilily a conspirator . Because
of the known conspirators in the company, to find the other suspects in the network sturcture, A
list of association degree is ordered with ascending. At last, on the basis of the computational
model, taking transmission information among members into acount, we stratified these message
topics and given the different layers of different topics of suspicious degrees. According to the
different topics among these members, we can calculate their suspicious degrees accurately and
ordered them descending. Analysis by synthesis the result of three sort, using a computermerge
the result of three sort, thereby all of the suspects is obtained accurately.
Computers is the efficient and accurate tools to handle and analyze large-scale data. Using
computers to deal with original data can reduce the error of the value of topics. We can make full
use of semantic network analysis, artificial intelligence and text analysis, and calculate the
frequency of words in original data . There are some less important but greater frequent words
among those data, such as copula and personal pronouns. Deleting this type of words and merge
them with similar semantic words, a language bank is then built with these similar words, and the
frequency of every words is computed. Sometimes words collocation in the bank are also
suspicious. therefore when these suspicious words appear and the frequency of them is high, thus
the person who transmit the type of information must be suspicious characters. It suffice to find
all of suspects accurately by this method.
The crime busting model we built can also apply to many other practical cases. For example, it
can be used on detecting difficult network-crime cases and applied to the problem of the spread of
the virus between cells in the biological network. The method can also deal with these difficulties
with high accuracy.
Locating the conspirators in the company Abstract The mathematical model built in this paper can analyze the complex crime reasonably. According to a part of known arrested suspects and their social relationship, the model can discover the conspirator with the maximum possibility, which can accelerate the process of investigating cases With the the suspicious information, known conspirators and innocents, a model of finding internal crime suspects in the company is established. For a person in the company, if he has closer contact with other members he will has more possibility to commit a crime, which is called core degree. Making use of Matrix replacement method and Hierarchical clustering method, we can find the group of higher core degree and sort them with descending. Next, we deem that a member who has closer contact with a conspirator will be more possibilily a conspirator . Because of the known conspirators in the company, to find the other suspects in the network sturcture, A list of association degree is ordered with ascending. At last, on the basis of the computational model, taking transmission information among members into acount, we stratified these message topics and given the different layers of different topics of suspicious degrees. According to the different topics among these members, we can calculate their suspicious degrees accurately and ordered them descending. Analysis by synthesis the result of three sort, using a computer merge the result of three sort, thereby all of the suspects is obtained accurately. Computers is the efficient and accurate tools to handle and analyze large-scale data. Using computers to deal with original data can reduce the error of the value of topics. We can make full use of semantic network analysis, artificial intelligence and text analysis, and calculate the frequency of words in original data . There are some less important but greater frequent words among those data, such as copula and personal pronouns. Deleting this type of words and merge them with similar semantic words, a language bank is then built with these similar words, and the frequency of every words is computed. Sometimes words collocation in the bank are also suspicious. therefore when these suspicious words appear and the frequency of them is high, thus the person who transmit the type of information must be suspicious characters. It suffice to find all of suspects accurately by this method. The crime busting model we built can also apply to many other practical cases. For example, it can be used on detecting difficult network-crime cases and applied to the problem of the spread of the virus between cells in the biological network. The method can also deal with these difficulties with high accuracy. Keywords: core degrees, suspicious degrees, association degrees, text analysis, crime busting
Team#15783 page 2 of 15
Contents
1 Introduction ............................................................................................................... 3
2 Analysis of the Problem ............................................................................................ 3
3 Crime busting ............................................................................................................ 4
3.1 Analysis ............................................................................................................ 4
3.2 Symbols ............................................................................................................ 4
3.3 Assumption ...................................................................................................... 5
3.4 Modeling .......................................................................................................... 5
3.4.1 Solving of the core degree ..................................................................... 5
3.4.2 Solving of absolutely close degree ........................................................ 6
3.4.3 Using suspected information to find criminal suspects ..................... 7
3.5 Consider the additional datas ........................................................................ 9
4 Computer processing .............................................................................................. 10
4.1 Analysis .......................................................................................................... 10
4.2 Definition ....................................................................................................... 11
4.3 Computer processing Method ...................................................................... 11
5 Model promotion ..................................................................................................... 13
5.1 Analysis of model .......................................................................................... 13
5.2 Model application.......................................................................................... 13
6 Weaknesses and Strengths of the Model ............................................................... 14
6.1 Strengths ........................................................................................................ 14
6.2 Weaknesses .................................................................................................... 14
7 Conclusion ............................................................................................................... 15
8 References ................................................................................................................ 15
Team#15783 page 3 of 15
1 Introduction
In recent years, the group frauds and economic crime problems have been very common in our daily life. Since the number of suspects is great, then it is difficult to detect the cases for the public security organs. Once the criminals escape, they will be a threat to more people and property, and even to personal security . The links between criminals posed by criminal networks is complex , and the mistake of judgment will falsely accuse innocent people and let the criminals get off . Here the problem arises: how to locate all the suspicion without a mistake? We learned many of the existing methods find that none give us a definite answer. Then we found the model of crime busting is similar to the model of social networks in the BBS in a way. What we need to do is identify the core characters and important figures in this matter. If we do the above, then we can basically determine the degree of suspicion for each person in this group combined with all kinds of the message topics, and arrange the guesswork out of suspects. It narrow the scope of criminal elements and reduce the workload of the public security organs so that they can break cases faster.
2 Analysis of the Problem
First, we should gain a clear idea of the characteristics of internal crime. There is a wide range of the criminal subjects: the senior managers of the company can be the conspirator, and each of the staffs can be also the conspirator. So it is difficult to capture criminals. But if we use the key features of economic crime, then we can easily find the mastermind of their accomplices.
According to the characteristics of crime and the offender's psychological, we believe that the possibility that those persons who keep close contact with criminals are the conspirators is very large. Then, by known criminals and suspicious messages, we can accurately determine criminal conspirators.
Based on the known contacts between persons inside the company, we use the matrix displacement method and the hierarchical clustering method to identify the company's central figures who keep close contact with other employees. Then combining with these figures with the types of message topics, we can make a further judgment to identify the other suspects. In addition, using given already certain schemers and certain not schemers, we can further discuss the group which have close relationship with those persons, and so we can increase the veracity of the result. With the help of our model, we can identify the maximum possible accomplices and confidently point out the conspirators and accomplices within the company.
Team#15783 page 4 of 15
3 Crime busting
3.1 Analysis
Although criminals choose randomly the modality of crime information and the route
transmission of crime information, but their crime form still has a certain rule. Their
modus operandi in this company transmits mainly by the information transfer form, so
criminal gangs should form a network connection model. Questions have given
several already certain criminals and crime information types, then according to these
criminals' close relationship and the suspected degrees of total numbers of message
receive and message issued, we can judge the scope of the criminals.
3.2 Symbols
Table1 symbols
k A The number of the company group members
ai, k if member i and member k have directly connected
D C k Connection degree of each member k A
ij g k if means of member i A and member j A pass by k A .
C k B k A ’s absolute agent degree
li, k the most short-circuit path length of member i A and member j A
C C k the absolutely tight density
D C the average value of the absolute agent degree
B C the average value of the absolute agent degree
C C the average value of the closely degree
D Dev C the smooth coefficient of connection degree
B Dev C the smooth coefficient of absolute agent
C Dev C the smooth coefficient of absolutely tight density
Team#
The mathematical model built in this paper can analyze the complex crime reasonably. According
to a part of known arrested suspects and their social relationship, the model can discover the
conspirator with the maximum possibility, which can accelerate the process of investigating cases
With the the suspicious information, known conspirators and innocents, a model of finding
internal crime suspects in the company is established. For a person in the company, if he has
closer contact with other members he will has more possibility to commit a crime, which is called
core degree. Making use of Matrix replacement method and Hierarchical clustering method, we
can find the group of higher core degree and sort them with descending. Next, we deem that a
member who has closer contact with a conspirator will be more possibilily a conspirator . Because
of the known conspirators in the company, to find the other suspects in the network sturcture, A
list of association degree is ordered with ascending. At last, on the basis of the computational
model, taking transmission information among members into acount, we stratified these message
topics and given the different layers of different topics of suspicious degrees. According to the
different topics among these members, we can calculate their suspicious degrees accurately and
ordered them descending. Analysis by synthesis the result of three sort, using a computermerge
the result of three sort, thereby all of the suspects is obtained accurately.
Computers is the efficient and accurate tools to handle and analyze large-scale data. Using
computers to deal with original data can reduce the error of the value of topics. We can make full
use of semantic network analysis, artificial intelligence and text analysis, and calculate the
frequency of words in original data . There are some less important but greater frequent words
among those data, such as copula and personal pronouns. Deleting this type of words and merge
them with similar semantic words, a language bank is then built with these similar words, and the
frequency of every words is computed. Sometimes words collocation in the bank are also
suspicious. therefore when these suspicious words appear and the frequency of them is high, thus
the person who transmit the type of information must be suspicious characters. It suffice to find
all of suspects accurately by this method.
The crime busting model we built can also apply to many other practical cases. For example, it
can be used on detecting difficult network-crime cases and applied to the problem of the spread of
the virus between cells in the biological network. The method can also deal with these difficulties
with high accuracy.
Locating the conspirators in the company Abstract The mathematical model built in this paper can analyze the complex crime reasonably. According to a part of known arrested suspects and their social relationship, the model can discover the conspirator with the maximum possibility, which can accelerate the process of investigating cases With the the suspicious information, known conspirators and innocents, a model of finding internal crime suspects in the company is established. For a person in the company, if he has closer contact with other members he will has more possibility to commit a crime, which is called core degree. Making use of Matrix replacement method and Hierarchical clustering method, we can find the group of higher core degree and sort them with descending. Next, we deem that a member who has closer contact with a conspirator will be more possibilily a conspirator . Because of the known conspirators in the company, to find the other suspects in the network sturcture, A list of association degree is ordered with ascending. At last, on the basis of the computational model, taking transmission information among members into acount, we stratified these message topics and given the different layers of different topics of suspicious degrees. According to the different topics among these members, we can calculate their suspicious degrees accurately and ordered them descending. Analysis by synthesis the result of three sort, using a computer merge the result of three sort, thereby all of the suspects is obtained accurately. Computers is the efficient and accurate tools to handle and analyze large-scale data. Using computers to deal with original data can reduce the error of the value of topics. We can make full use of semantic network analysis, artificial intelligence and text analysis, and calculate the frequency of words in original data . There are some less important but greater frequent words among those data, such as copula and personal pronouns. Deleting this type of words and merge them with similar semantic words, a language bank is then built with these similar words, and the frequency of every words is computed. Sometimes words collocation in the bank are also suspicious. therefore when these suspicious words appear and the frequency of them is high, thus the person who transmit the type of information must be suspicious characters. It suffice to find all of suspects accurately by this method. The crime busting model we built can also apply to many other practical cases. For example, it can be used on detecting difficult network-crime cases and applied to the problem of the spread of the virus between cells in the biological network. The method can also deal with these difficulties with high accuracy. Keywords: core degrees, suspicious degrees, association degrees, text analysis, crime busting
Team#15783 page 2 of 15
Contents
1 Introduction ............................................................................................................... 3
2 Analysis of the Problem ............................................................................................ 3
3 Crime busting ............................................................................................................ 4
3.1 Analysis ............................................................................................................ 4
3.2 Symbols ............................................................................................................ 4
3.3 Assumption ...................................................................................................... 5
3.4 Modeling .......................................................................................................... 5
3.4.1 Solving of the core degree ..................................................................... 5
3.4.2 Solving of absolutely close degree ........................................................ 6
3.4.3 Using suspected information to find criminal suspects ..................... 7
3.5 Consider the additional datas ........................................................................ 9
4 Computer processing .............................................................................................. 10
4.1 Analysis .......................................................................................................... 10
4.2 Definition ....................................................................................................... 11
4.3 Computer processing Method ...................................................................... 11
5 Model promotion ..................................................................................................... 13
5.1 Analysis of model .......................................................................................... 13
5.2 Model application.......................................................................................... 13
6 Weaknesses and Strengths of the Model ............................................................... 14
6.1 Strengths ........................................................................................................ 14
6.2 Weaknesses .................................................................................................... 14
7 Conclusion ............................................................................................................... 15
8 References ................................................................................................................ 15
Team#15783 page 3 of 15
1 Introduction
In recent years, the group frauds and economic crime problems have been very common in our daily life. Since the number of suspects is great, then it is difficult to detect the cases for the public security organs. Once the criminals escape, they will be a threat to more people and property, and even to personal security . The links between criminals posed by criminal networks is complex , and the mistake of judgment will falsely accuse innocent people and let the criminals get off . Here the problem arises: how to locate all the suspicion without a mistake? We learned many of the existing methods find that none give us a definite answer. Then we found the model of crime busting is similar to the model of social networks in the BBS in a way. What we need to do is identify the core characters and important figures in this matter. If we do the above, then we can basically determine the degree of suspicion for each person in this group combined with all kinds of the message topics, and arrange the guesswork out of suspects. It narrow the scope of criminal elements and reduce the workload of the public security organs so that they can break cases faster.
2 Analysis of the Problem
First, we should gain a clear idea of the characteristics of internal crime. There is a wide range of the criminal subjects: the senior managers of the company can be the conspirator, and each of the staffs can be also the conspirator. So it is difficult to capture criminals. But if we use the key features of economic crime, then we can easily find the mastermind of their accomplices.
According to the characteristics of crime and the offender's psychological, we believe that the possibility that those persons who keep close contact with criminals are the conspirators is very large. Then, by known criminals and suspicious messages, we can accurately determine criminal conspirators.
Based on the known contacts between persons inside the company, we use the matrix displacement method and the hierarchical clustering method to identify the company's central figures who keep close contact with other employees. Then combining with these figures with the types of message topics, we can make a further judgment to identify the other suspects. In addition, using given already certain schemers and certain not schemers, we can further discuss the group which have close relationship with those persons, and so we can increase the veracity of the result. With the help of our model, we can identify the maximum possible accomplices and confidently point out the conspirators and accomplices within the company.
Team#15783 page 4 of 15
3 Crime busting
3.1 Analysis
Although criminals choose randomly the modality of crime information and the route
transmission of crime information, but their crime form still has a certain rule. Their
modus operandi in this company transmits mainly by the information transfer form, so
criminal gangs should form a network connection model. Questions have given
several already certain criminals and crime information types, then according to these
criminals' close relationship and the suspected degrees of total numbers of message
receive and message issued, we can judge the scope of the criminals.
3.2 Symbols
Table1 symbols
k A The number of the company group members
ai, k if member i and member k have directly connected
D C k Connection degree of each member k A
ij g k if means of member i A and member j A pass by k A .
C k B k A ’s absolute agent degree
li, k the most short-circuit path length of member i A and member j A
C C k the absolutely tight density
D C the average value of the absolute agent degree
B C the average value of the absolute agent degree
C C the average value of the closely degree
D Dev C the smooth coefficient of connection degree
B Dev C the smooth coefficient of absolute agent
C Dev C the smooth coefficient of absolutely tight density
Team#
