BeWitness

                                         BeWitness - An App for Public Safety

                                                         Version 3.0

BeWitness is an app that keeps in mind public privacy and safety first. We believe using advanced technology and making public part of it will reduce crime and a safer society.

This app is scalable world wide.

Please do download it from the link attached and spread the word and be a part of a safer society.

                 https://play.google.com/store/apps/details?id=eyewitness.com.eyewitness

This app helps users to record crime incidents and receive alert notifications of crime incidents near them with accurate time, type, location i.e, latitude & longitude with additional optional information and direction to the location of the incident from the user's current location. – say within 5 km surrounding. Users can choose to subscribe/unsubscribe for certain type of incident notifications for e.g, Vehicle theft, Hit & Run, Murder, Accident etc.

Apart from receiving notifications, the user of BeWitness will be able to view the location, time and type of incidents occurred 10 KM around them in the last 24 hours. The geolocation of the incident on the Google map can be shared with other mobile users for coordinating help

                                     

In case, if the user, unfortunately, becomes a victim of a crime incident, he/she can quickly reach out to police/ fire/ambulance on a press of a button from the emergency list. Provisions to add 4 mobile numbers into the emergency contact list is provided. In case of emergency, pressing an emergency button will send an emergency message via SMS with the current location of the user and initiates a phone call. The receiver of the SMS message can click on the location link to get the direction to reach out to the incident location with the help of Google maps shortest route.

Web Application

                                                                    Eye Witness
                                            developed by Gayathri H and Balaji A.T    

                  We developed this Web Application as a part of our internship in Logic Research Labs during IInd year of our B.E. program.

                 "Eye Witness" a Web Application for the police, the ambulance service and the fire service. The main idea is to service the people who reported the crime witnessed in their area or in some location nearer to them anonymously through "Be Witness" an Android Application.

The "Home" page contains a form with the fields Latitude, Longitude, From Date, To Date and Incidents.
                    ->Initially based on the current location (provided the gps is on)of the system the latitude and the longitude are automatically filled in the corresponding fields. Based on requirements we can manually change those fields.

                   
               
                  ->we can either type the From Date and To Date or select from the date picker.

                    ->we can select the particular incidents from the dropdown checkboxes.
                    ->And then click the "show" button to generate record and view the map.

After clicking the "show" button, the data is fetched from the Firebase database which connected to the Android Application "Be Witness".Now the Crime Report and the Crime Hotspots Map is generated from the data.

Crime Report:
             Crime Report shows the table below which contains fields like Incident Type, Latitude, Longitude, Date and Time.The main purpose of this report is to analyse
            ->where and in what time the maximum crime taking place
            ->what kind of crime happening more often in a particular area
            ->how to improve the service for the people
            ->what kind of actions can be taken to reduce crime in the particular area, etc.,

               
Crime Hotspots:
              Crime Hotspots contains the map filled with markers in the place where the crime is reported by the people. Clicking the marker will show the tool tip which contains the information like  Latitude, Longitude,Incident Type, Date and Time.
              If the reported crime time is within one hour from the current time.The marker will jump in that location.So that the police, the ambulance service and the fire service can take immediate actions by contacting the nearest service centers from the crime location.

Text Categorization

                                                            Mini Project

       Lexical relations or semantic relations of words are useful knowledge fundamental to all applications since they help to capture inherent semantic variations of vocabulary in human languages. Discovering such knowledge in a robust way from arbitrary text data is a significant challenge in big text data mining. I propose a novel  method using wordnet corpus to systematically mine fundamental and complementary lexical relation, I.e., Paradigmatic relations between words from arbitrary text data.

MINING WORD ASSOCIATIONS:
    There are two common types of word associations in natural language processing, paradigmatic and syntagmatic:

Paradigmatic: words A and B are paradigmatically related if they can be substituted for each other. This indicates they belong in the same class, such as "Monday" and "Thursday" or "cat" and "dog".

Syntagmatic: words that can be combined with each other, such as "cold" and "weather".
Both paradigmatic and syntagmatic relations are very useful knowledge fundamental to various applications involving text processing, including, e.g., search engines, text classification. For example, such relations can be directly useful in search engine applications to enrich the representation of a query or suggest related queries and for capturing inexact matching of text for classification or clustering.

BLOCK DIAGRAM:

INPUT:

csv file with columns user and sentence from each user.

FUNCTIONS IN BLOCK DIAGRAM:

TOKENIZATION:
    Tokenization is the process of splitting a user input sentence into a words.
     
STOPWORD REMOVAL:
    Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words .
    Stop word removal will remove the stopwords like is, a, the, for, have, the, it etc.

WORD SYNSET:
      Word synset will find synsets for the tokens. Here the tokens as nouns will have different meanings and verb in language dictionary are declared here as noun in word net dictionary.We have to handle those cases. Example : ’like’ is a verb but it has noun also as per in wordnet corpus and also ‘like’ as a verb has many different definitions, we have to find which matches the best. Synsets not only gives information about the word ‘like’ but also its synonyms eg: alike, comparable.We have to handle those cases.

POS TAGGING:
     POS tagging is used to filter the synset  to pick the synsets with  nouns which have meaningful definition in semantic level. With the help of wordnet corpus we can get the pos for a words.Here we use pos tagging for extracting noun from the list of synsets.

PATTERN MATCHING:
     We need this step because, the synset for the word like as we saw as example for word synset will give not only synsets for the word ‘like’ but also gives synset for its synonyms.We have to remove these synonyms for the word 'like'.
     Pattern matching is the process of matching the nouns extracted using pos from wordnet corpus with the nouns in the input tokens.

MAPPING TO GET DESTINATION:
    Our main idea is to extract category from the definitions of the words which are nouns.So using wordnet corpus map the words with its definitions and give output as dictionary.
Mapping is the process of getting definitions for the nouns from the wordnet corpus.

COSINE SIMILARITY:
    Cosine similarity is used to measure similarity between two sentences.

TOKENIZING THE DEFINITION:
    Tokenizing the similar definition and passing to the stop word removal to filter the patterns.

STOP WORD REMOVAL FOR TOKENIZED DEFINITION:
    An input is in the form of tokens of definitions and output is tokens with stop words removed.

CATEGORIZATION:
    Here categorization is done by matching the tokens between the tokens of two definitions.

LEXICAL GRAPH:
    Lexical graph is the graph between user and the categories in the user input.

APPLICATIONS:
    Paradigmatic relations are very useful knowledge fundamental to various applications involving text processing including e.g, search engines, recommendation systems, text classification, text summarization, text analytics. For example, such relations can be directly useful in search engine applications to enrich the representation of a query or suggest related queries and for capturing inexact matching of text for classification or cluster

OUTPUT:

have attached graph for users 2,5 and 7