Network-aware recommendations in online social networks

Along with the rapid increase of using social networks sites such as Twitter, a massive number of tweets published every day which generally affect the users decision to forward what they receive of information, and result in making them feel overwhelmed with this information. Then, it is important for this services to help the users not lose their focus from what is close to their interests, and to find potentially interesting tweets. The problem that can occur in this case is called information overload, where an individual will encounter too much information in a short time period. For instance, in Twitter, the user can see a large number of tweets posted by her followees. To sort out this issue, recommender systems are used to give contents that match the user's needs. This thesis presents a tweet-recommendation approach aiming at proposing novel tweets to users and achieving improvement over baseline. For this reason, we propose to exploit network, content, and retweet analyses for making recommendations of tweets. The main objective of this research is to recommend tweets that are unseen by the user (i.e., they do not appear in the user timeline) because nobody in her social circles published or retweeted them. To achieve this goal, we create the user's ego-network up to depth two and apply the transitivity property of the \emph{friends-of-friends} relationship to determine interesting recommendations. After this step, we apply cosine similarity and Jaccard distance as similarity measures for the candidate tweets obtained from the network analysis using bigrams. We also count the mutual retweets between the ego user and candidate users as a measure of shared similar tastes. The values of these features are compared together for each of the candidate tweets using pairwise comparisons in order to determine interesting recommendations that are ranked to best match the user's interests. Experimental results demonstrate through a real user study that our approach improves the state-of-the-art technique. In addition to the efficiency of our approach in finding relevant contents, it is also characterized by the fact of providing novel tweets, which solves the over-specialization challenge or serendipity problem that appears when using content-based recommender systems as a stand alone approach of recommendation

Complex network tools to enable identification of a criminal community

Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal association from enormous amount of raw data and representing them explicitly is tedious and time consuming. A study of the complex networks literature reveals that graph-based detection methods have not, as yet, been used for money laundering detection. In this research, I explore the use of complex network analysis to identify the money laundering criminals’ communication associations, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path. For this purpose, I use the publicly available Enron email database that happens to contain the communications of 10 criminals who were convicted of a money laundering crime. I show that my new shortest paths network search algorithm (SPNSA) combining shortest paths and network centrality measures is better able to isolate and identify criminals’ connections when compared with existing community detection algorithms and k-neighbourhood detection. The SPNSA is validated using three different investigative scenarios and in each scenario, the criminal network graphs formed are small and sparse hence suitable for further investigation. My research starts with isolating emails with ‘BCC’ recipients with a minimum of two recipients bcc-ed. ‘BCC’ recipients are inherently secretive and the email connections imply a trust relationship between sender and ‘BCC’ recipients. There are no studies on the usage of only those emails that have ‘BCC’ recipients to form a trust network, which leads me to analyse the ‘BCC’ email group separately. SPNSA is able to identify the group of criminals and their active intermediaries in this ‘BCC’ trust network. Corroborating this information with published information about the crimes that led to the collapse of Enron yields the discovery of persons of interest that were hidden between criminals, and could have contributed to the money laundering activity. For validation, larger email datasets that comprise of all ‘BCC’ and ‘TO/CC’ email transactions are used. On comparison with existing community detection algorithms, SPNSA is found to perform much better with regards to isolating the sub-networks that contain criminals. I have adapted the betweenness centrality measure to develop a reliance measure. This measure calculates the reliance of a criminal on an intermediate node and ranks the importance level of each intermediate node based on this reliability value. Both SPNSA and the reliance measure could be used as primary investigation tools to investigate connections between criminals in a complex network

A Simulation Environment with Reduced Reality Gap for Testing Autonomous Vehicles

In order to facilitate acceptance and ensure safety, autonomous vehicles must be tested not only in typical and relatively safe scenarios but also in dangerous and less frequent scenarios. Recent pedestrian fatalities caused by test vehicles of the front-running giants like Google and Tesla suffice the fact that Autonomous Vehicle technology is not yet mature enough and still needs rigorous exposure to a wide range of traffic, landscape, and natural conditions on which the Autonomous Vehicles can be trained on to perform as expected in real traffic conditions. Simulation Environments have been considered as an efficient, safe, flexible and cost-effective option for the training, testing, and validation of Autonomous Vehicle technology. While ad-hoc task-specific use of simulation in Autonomous Driving research is widespread, simulation platforms that bridge the gap between simulation and reality are limited. This research proposes to set up a highly realistic simulation environment (using CARLA driving simulator) to generate realistic data to be used for Autonomous Driving research. Our system is able to recreate the original traffic scenarios based on prior information about the traffic scene. Furthermore, the system will allow to make changes to the original scenarios and create various desired testing scenarios by varying the parameters of traffic actors, such as location, trajectory, speed, motion states, etc. and hence collect more data with ease

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments