Cyber security challenges in Smart Cities: Safety, security and privacy

The world is experiencing an evolution of Smart Cities. These emerge from innovations in information technology that, while they create new economic and social opportunities, pose challenges to our security and expectations of privacy. Humans are already interconnected via smart phones and gadgets. Smart energy meters, security devices and smart appliances are being used in many cities. Homes, cars, public venues and other social systems are now on their path to the full connectivity known as the “Internet of Things.” Standards are evolving for all of these potentially connected systems. They will lead to unprecedented improvements in the quality of life. To benefit from them, city infrastructures and services are changing with new interconnected systems for monitoring, control and automation. Intelligent transportation, public and private, will access a web of interconnected data from GPS location to weather and traffic updates. Integrated systems will aid public safety, emergency responders and in disaster recovery. We examine two important and entangled challenges: security and privacy. Security includes illegal access to information and attacks causing physical disruptions in service availability. As digital citizens are more and more instrumented with data available about their location and activities, privacy seems to disappear. Privacy protecting systems that gather data and trigger emergency response when needed are technological challenges that go hand-in-hand with the continuous security challenges. Their implementation is essential for a Smart City in which we would wish to live. We also present a model representing the interactions between person, servers and things. Those are the major element in the Smart City and their interactions are what we need to protect

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

Abstract A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies

Intentionality Analysis in 3D Games

International audienc

EFFECT OF SAMPLING SIZE ON DATA MINING USING ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks (ANNs) represent a useful technique for data mining applications. They can be trained to properly represent various categories occurring in a data set. In large databases, and data warehousing techniques, the size of data sets can be huge which may result in inefficient ANNs learning. Thus, it is useful to find an efficient and practical training set size without compromising the results. This paper presents experimental results highlighting the effect of varying the sampling size used in training Artificial Neural Networks and demonstrates that the extra effort used in expanding the training set is not linearly proportional to the improved accuracy. These results are important, and they are currently validated on a variety of domains

Levels, solid-phase fractions and sources of heavy metals at site received industrial effluents: a case study

Heavy metals in the site received industrial effluents were investigated to assess the pollution levels, distribution of metal among solid-phase fractions and possible metal sources. The soil samples at different depths of 0–5, 5–25 and 25–50 cm were collected and analyzed for Fe, Mn, Cd, Zn, Cu, Ni and Pb. Among all metals, Cd content was not detected in all soil samples. The average contents of Pb and Zn are higher than the corresponding values of common range in earth crust. Meanwhile, the maximum contents of Cu and Zn are higher than those of Dutch optimum value but lower that the Dutch protection act target value. The maximum contents of Cu, Pb and Zn are higher than the average shale value. The most investigated heavy metals are mostly found in the potentially labile pool (>50.0%) including metal bound to carbonate, Fe/Mn oxides, or organically fractions. Enrichment factor (EF) in combination with multivariate analysis including principal component analysis (PCA) and hierarchical cluster analysis (HCA) suggest that Mn and Ni associated with Fe in the soil samples were primarily originated from lithogenic sources. Pb was largely derived only from anthropogenic source, while Cu and Zn in the soil samples were controlled by the mixed natural and anthropogenic sources. These results suggest that discharging the industrial effluents into dumping site increased pollution level of Pb, Zn and Cu as well as enhanced their potentially labile pool that may be responsible for occurring potential toxic impacts on environmental quality

Affective Virtual Reality Gaming for Autism

Emotional impairment is one of the common symptoms of many mental diseases. Being able to learn the emotional reactions from subjects using nonintrusive human-computer interactions (HCI) would provide a novel and efficient approach to assist existing intervention and therapy. Psychologists conducted research using virtual reality (VR) as a tool for exposure starting from decades ago. However, early VR equipment was cumbersome in size and inefficient, which can only be used to simulate limited scenes, such as car driving and phobia scenarios. With the evolution of affordable and portable VR hardware, we are now able to design systematic VR games that can precisely control variables for different stimuli and testing cases. Furthermore, the VR nowadays can serve not only as exposure methods, but also real games that seamlessly and nonintrusively interact with users. The experience of immersion and presence has made VR naturally suitable for triggering strong emotions. Moreover, the study of affective computing, known as the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, is a trendy and challenging topic in HCI field. Given current research and potential development, affective computing is seeking to develop emotional intelligence in machines. Integrating the concept of affective computing into game design and development will lead to a new type of serious games that interact with users’ emotions. This chapter reviews the methodologies commonly used in affective computing and related research projects using VR exposure as an intervention for people with special needs. It also describes a series of studies conducted to collect and analyze data. Our goal is to propose a game framework that recognizes users’ emotional reactions in a multimodal approach, which, with ideal expectation, adapts according to the fluctuation of the users’ emotional states dynamically. The system provides a guideline for affective gaming design for mental healthcare purposes. The application of the framework is to assist in intervention for autistic spectrum disorder and can be extended to other emotion-related mental illness