WECIA Graph: Visualization of Classification Performance Dependency on Grayscale Conversion Setting

Grayscale conversion is a popular operation performed within image pre-processing of many computer vision systems, including systems aimed at generic object categorization. The grayscale conversion is a lossy operation. As such, it can signicantly in uence performance of the systems. For generic object categorization tasks, a weighted means grayscale conversion proved to be appropriate. It allows full use of the grayscale conversion potential due to weighting coefficients introduced by this conversion method. To reach a desired performance of an object categorization system, the weighting coefficients must be optimally setup. We demonstrate that a search for an optimal setting of the system must be carried out in a cooperation with an expert. To simplify the expert involvement in the optimization process, we propose a WEighting Coefficients Impact Assessment (WECIA) graph. The WECIA graph displays dependence of classication performance on setting of the weighting coefficients for one particular setting of remaining adjustable parameters. We point out a fact that an expert analysis of the dependence using the WECIA graph allows identication of settings leading to undesirable performance of an assessed system

An Approach for Analyzing Cyber Security Threats and Attacks: A Case Study of Digital Substations in Norway

In this paper, we provide an approach for analyzing cyber security threats and attacks in digital substations, which is based on several steps we performed within our work on two Research Council of Norway (RCN) projects. In the literature, there are various separate or theoretical concepts to understand and follow a security analysis of smart grids in general, but none is focused specifically on digital substations. Moreover, none is showing real applicability on an existing use case, making the implementation difficult. The approach we propose here is a result of our attempts to create a comprehensive overview of the individual steps we have been taking to do the analysis. For that reason, firstly, we start with defining and explaining a digital substation and its concepts, and the security challenges related to digital substations. Afterwards, we present the main steps of the security analysis for digital substation. The first step is the security pyramid. The following steps are threat analysis, threat modeling, risk assessment and the simulation impact analysis, which are another contribution from our group presented in this paper. Considering that the main goal of a security analysis is to create awareness for the stakeholders of digital substations, such an impact simulation provides a flexible way for stakeholders to see and to understand the consequences of security threats and attacks. We summarize the paper with an illustration of the steps we are taking in the form of the approach for digital substation

Criminal Network Community Detection in Social Media Forensics

Nowadays, Online Social Networks (OSNs) has created a breeding ground for criminals to engage in cyber–crime activities, and the legal enforcement agencies (LEAs) are facing significant challenges since there is no consistent and generalized framework built specifically to analyse users’ misbehaviour and their social activity on these platforms. Data exchanged over these platforms represent an important source of information, even their characteristics such as unstructured nature, high volumes, velocity, and data inter–connectivity, become an obstacle for LEAs to analyse these data using traditional methods in order to provide it to the legal domain. Although numerous researches have been carried out on digital forensics, little focus has been employed on developing appropriate tools to exhaustively meet all the requirements of crime investigation targeting data integration, information sharing, collection and preservation of digital evidences. To bridge this gap, in our preliminary work we presented a generic digital evidence framework, called CISMO as a semantic tool that is able to support LEAs in detecting and preventing different type of crimes happening on OSNs. This paper gives details of the knowledge extraction layer of the framework. Specially, we mainly focus on analyses criminal social graph structures proving the effectiveness of CISMO in a case study with real criminal dataset. Experimental results reveal that applying appropriate Social Network Analyses (SNA), CISMO framework should be able to query and discover the criminal networks, empowering the criminal investigator to see the connections between people

Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study

We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature

Security Analysis of Wireless BAN in e-Health

The Wireless Body Area Network (WBAN) has gained popularity as a new technology for e-Health, and is considered as one of the key research areas in computer science and healthcare applications. WBAN collects patients’ data, monitors constantly their physiological parameters, using small implantable or wearable sensors, and communicates these data using wireless communication techniques in short range. WBAN is playing a huge role in improving the quality of healthcare. Still, due to sensitive and concurrent nature of e-Heath systems, current research has showed that designers must take into considerations the security and privacy protection of the data collected by a WBAN to safeguard patients from different exploits or malicious attacks, since e-Health technologies are increasingly connected to the Internet via wireless communications. In this paper we outline the most important security requirements for WBANs. Furthermore, we discuss key security threats to avoid. Finally, we conclude with a summary of security mechanisms to follow that address security and privacy concerns of WBANs, and need to be explored in an increasingly connected healthcare world

“A Privacy and Data Protection Best Practices for Biometrics Data Processing in Border Control: Lesson Learned from SMILE

Biometric recognition is a highly adopted technology to support different kinds of applications, ranging from security and access control applications to low enforcement applications. However, such systems raise serious privacy and data protection concerns. Misuse of data, compromising the privacy of individuals and/or authorized processing of data may be irreversible and could have severe consequences on the individual’s rights to privacy and data protection. This is partly due to the lack of methods and guidance for the integration of data protection and privacy by design in the system development process. In this paper, we present an example of privacy and data protection best practices to provide more guidance for data controllers and developers on how to comply with the legal obligation for data protection. These privacy and data protection best practices and considerations are based on the lessons learned from the SMart mobILity at the European land borders (SMILE) project

Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance

© 2016 IEEE. We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentation. It is intended for research scenarios related to safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial domain. The task of human and object modeling has been used for detecting activity, and for inferring and predicting actions, different from those works, we do human and object modeling in order to learn interactions in RGB-D data for improving segmentation. For this purpose, we define a novel density function to model a three dimensional (3D) scene in a virtual environment (VREP). This density function takes into account various possible configurations of human-object and object-object relationships and interactions governed by their affordances. Using this function, we synthesize a large, realistic and highly varied synthetic RGB-D dataset that we use for training. We train a random forest classifier, and the pixelwise predictions obtained is integrated as a unary term in a pairwise conditional random fields (CRF). Our evaluation shows that modeling these interactions improves segmentation performance by ∼7% in mean average precision and recall over state-of-the-art methods that ignore these interactions in real-world data. Our approach is computationally efficient, robust and can run real-time on consumer hardware.Sharma V., Yildirim-Yayilgan S., Van Gool L., ''Low-cost scene modeling using a density function improves segmentation performance'', 25th IEEE international symposium on robot and human interactive communication - RO-MAN 2016, August 26-31, 2016, New York, USA.status: publishe

The multi-objective feature selection in Android malware detection system

The Android operating system boosts a global market share over the previous years, which has made it the most popular operating system in the world. Recently, Android has become the target of attacks by cybercriminals because of its open-source code and its progressive growth. Many machine learning techniques have been used to address this issue in the Android operating system. However, a limited range of feature selection methods has been used in these systems. This paper, therefore, aims to address and evaluate the impact of a multi-objective feature selection approach called NSGAII in Android malware detection systems. To improve the diversity of solutions offered by this method, we have modified the standard NSGAII approach. Experimental results show that the proposed method can lead to better malware classification