5153 research outputs found
Sort by
Enhanced Face Liveness Detection Based on Features From Nonlinear Diffusion Using Specialized Deep Convolution Network And Its Application In OAuth
No full textThe major contribution of this research is the development of enhanced algorithms that will prevent face spoofing attacks by utilizing a single image captured from a 2-D printed image or a recorded video. We first apply a nonlinear diffusion based on an additive operator splitting (AOS) scheme with a large time step to acquire a diffused image. The AOS-based scheme enables fast diffusion that successfully reveals the depth information and surface texture in the input image. Then a specialized deep convolution neural network is developed that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method yields higher accuracy as compared to the previously implemented state-of-the-art methods. As an application of the face liveness detection, we develop face biometric authentication in an Open Authorization (OAuth) framework for controlling secure access to web resources. We implement a complete face verification system that consists of face liveness detection followed by face authentication that uses Local Binary Pattern as features for face recognition. The entire face authentication process consists of four services: an image registration service, a face liveness detection service, a verification service, and an access token service for use in OAuth
Tracking Errors and Long-Run Performance of Leveraged ETFs
No full textLeveraged exchange-traded funds (ETFs) are relatively new to the world of investments but have become increasingly popular to aggressive investors. While a regular ETF tracks the value of a specific index of stocks, a leveraged ETF attempts to achieve a multiple of the return of the underlying index on a daily basis. This multiple can be positive in the case of bull ETFs or negative in the case of bear (or inverse) ETFs. To accomplish these objectives, leveraged and inverse funds pursue a range of investment strategies through the use of swaps, futures contracts, options and other derivative instruments. Due to the effect of compounding, operating expenses and daily resets, not to mention tracking errors, the performance of leveraged funds over longer periods of time can differ substantially from the performance (or inverse of the performance) of their underlying index or benchmark during the same period of time. Such performance deviations are often quite meaningful and unexpected over the long run. This paper provides an empirical assessment of how well the leveraged ETFs track their underlying index. The results show that the tracking errors on average are small. However, substantial tracking errors do occur from time to time. Despite the price decay associated with leveraged ETFs, their long-run performance
Enhancing Software Engineering Curricula By Incorporating Open, Data-Driven Planning Methods
No full textThe Software Engineering (SE) industry continues to experience high failure rates and missed business outcomes. Educators must ensure that SE course curricula and learning outcomes strive to create long-term positive impact. This research addresses how the planning process can be enhanced to make use of the vast amount of open source data that is now available to engineers. This can help improve the readiness levels of students in learning environments by integrating best practices that can address these issues in the context of SE course work. The SE course curricula, current standards, and intended learning outcomes are examined which enables us to outline the changes required. Recommendations and suggested methods to help close the gaps are outlined
Remote patient monitoring using safe and secure WBAN technology
No full textIn the recent years, we have witnessed a tremendous growth and development in the field of wireless communication technology and sensors. Resulting into opening new dimensions in various research fields. The integration of Nano scale devices with low power consumption circuits brought a new evolution in wireless networks. This blend of technologies led to the formation of a new field in WSN (Wireless Sensor Networks) known as WBAN (Wireless Body Area Network). WBAN is based on small sensors designed to operate and function mainly on the human body. As we are dealing with human lives, security and privacy are major concerns as patients’ data is at the stakes. Authentication is an important factor in securing information from unauthorized usage. Now-a-days a lot of research has been done in order to improve the overall authentication mechanisms in WBAN. In this poster, we are surveying the security challenges in WBAN with a focus on the authentication phase. A list of several methods along with their schemes has been studied and recapitulated. ECG is one the most popular schemes used in WBAN, benefiting from its uniqueness. However, it comes with challenges as creating an extract trait could get complicated. ECG could be aided by the help of combining fingerprint which will result in a non-destructive method of biometric authentication compared with single ECG trait
Gender Inequality in Algeria: How Women are Falling Behind
No full textAlgeria has been open to the conversation of the reformation of discriminatory laws over the years. However, despite these efforts, women in Algeria are still treated as second-class citizens. Since the reevaluation of discriminatory laws, women have received more rights and their participation in public life has increased in the country, but it is important to understand that this growth has not been so life-changing. In order to better understand the current circumstances in which Algerian women are living, this study aims to gather data relating to general laws and regulations that directly affect women, statistics in education to understand women's standings, political representation of women in the country, healthcare and family life
A Panoramic Study of Fall Detection Technologies
No full textFalls are a major risk of injury for elderly aged 65 or over, blind people, people with balance disorder and leg weakness. In this regard, assistive technology which aims to identify fall events at real time can reduce the rate of impairments and mortality. This study offer a literature research reference value for bioengineers for further research. Much of the past and the current fall detection research, the vital signals features and the way features are extracted and fed to a classifier are introduced. The study concludes with an assessment of the current technologies highlighting their critical limitations along with suggestions for future research direction in this rapidly developing field of study.http://dx.doi.org/10.20943/01201603.626
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
No full textSleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.https://doi.org/10.3390/e1809027
Flu Trend Prediction Using Social Media Network Data
No full textAli Al Essa's, Miad Faezipour's, Jeongkyu Lee's, and Gopala Duggina's poster using Hadoop and MapReduce programming to analyze Twitter data about influenza to analyze flu trends
Network intrusion detection using hardware techniques: A review
No full textThe increasing amount of network throughput and security threat makes intrusion detection a major research problem. In the literature, intrusion detection has been approached by either a hardware or software technique. This paper reviews and compares hardware based techniques that are commonly used in intrusion detection systems with a special emphasis on modern hardware platforms such as FPGA, GPU, many-core processors and ASIC. It also provides a detailed comparison between these hardware solution platforms. Our approach to classify modern hardware-based Intrusion Detection System (IDS) techniques is based on the detection approach. In addition, we provide a comparison between the classified detection approaches based on essential criteria such as definition, update process, detection ability, features of the system, and implementation requirements. Finally, a classification tree of hardware-based NIDS platforms is given
Crime Analysis of Chicago
No full textSecurity status in the place where we are live is one of the most important concerns for every individual. As most people may know, to evaluate the security status, criminal rate cannot be overlooked. Many factors can influence the occurrence of crimes including time, places, the construction of population, educational level, income level and so on. Nowadays, using modern technology such as Hadoop to process large volume of crime dataset, analyze the relationship between crime rate and the related factors and predict the happen of crime are getting more and more popular. In this project, using Hadoop ecosystem and related technologies, we choose crimes dataset of Chicago from 2001 to present to analyze the relationships between the occurrence of crimes and several key factors