Smart Detection of Cardiovascular Disease Using Gradient Descent Optimization

The Internet of Medical Things (IoMT) is the networking of health things or equipment that communicate data over the internet without the need for human involvement in the healthcare field. A large quantity of data is collected from numerous sensors in the health field, and it is all transferred and stored on the cloud. This data is growing bigger here all time, and it's becoming increasingly challenging to secure it on the cloud with real-time storage and computing. Data security problem can be addressed with the aid of machine algorithms and fog computing. For data security in IoMT gadgets correspondence in an intelligent fashion, an intelligent encryption algorithm (IEA) is proposed using blockchain technology in cloud based system framework (CBSF). It is applied on patient’s database to provide immutable security, tampering prevention and transaction transparency at the fog layer in IoMT.  The suggested expert system's results indicate that it is suitable for use in for the security. In the fog model, the blockchain technology approach also helps to address latency, centralization, and scalability difficulties

Microwave and Millimeter-Wave Remote Sensing for Security Applications. By Jeffrey A. Nanzer, Artech House, 2012; 372 pages. Price £109.00, ISBN 978-1-60807-172-2

Microwave and millimeter-wave remote sensing techniques are fast becoming a necessity in many aspects of security as detection and classification of objects or intruders becomes more difficult. This groundbreaking resource offers you expert guidance in this burgeoning area. It provides you with a thorough treatment of the principles of microwave and millimeter-wave remote sensing for security applications, as well as practical coverage of the design of radiometer, radar, and imaging systems. You learn how to design active and passive sensors for intruder detection, concealed object detection, and human activity classification. This detailed book presents the fundamental concepts practitioners need to understand, including electromagnetic wave propagation in free space and in media, antenna theory, and the principles of receiver design. You find in-depth discussions on the interactions of electromagnetic waves with human tissues, the atmosphere and various building and clothing materials. This timely volume explores recently developed detection techniques, such as micro-Doppler radar signatures and correlation radiometry. The book is supported with over 200 illustrations and 1,135 equations

Alternative Certification: A Research Brief

Alternative teacher certification (ATC) encompasses a broad range of programs that prepare teachers in non-traditional, accelerated ways (Suell and Piotrowski 2007). The number of teachers prepared through alternative routes has increased considerably in the past decade. As of 2011, 16% of public school teachers nationwide had entered the profession through some kind of alternative program, and in the last five years, 40% of new hires have come through ATC programs (Feistritzer 2011). In this brief I offer a short overview of research on the outcomes of alternative certification programs compared with traditional certification, summarize findings about what makes for effective alternative certification programs, and describe ATC programs in Alaska. Generally, ATC programs are aimed at people who are interested in becoming teachers and have at least a bachelor’s degree, as well as extensive life experience. But how these programs are defined and what they include varies considerably (Humphrey and Wechsler 2007). In this brief, alternative certification is defined as a program in which teacher candidates work as the instructor of record while completing their teacher certification. These programs are considered to be both a means of alleviating teacher shortages and a way of improving the quality of the teaching workforce. In addition to shortening the preparation time and being more flexible for working participants, ATC programs also typically incorporate mentoring (Mikulecky, Shkodriani et al. 2004; Scribner and Heinen 2009). The programs range from initiatives run by school districts and state departments of education to university-operated efforts run alongside traditional teacher preparation programs (Yao and Williams 2010)

Personal security in travel by public transport : the role of traveller information and associated technologies

Acknowledgement This research reported in this paper has been funded by a grant award from the Engineering and Physical Sciences Research Council: EP/I037032/1.Peer reviewedPublisher PD

Where has all the psychology gone? A critical review of evidence-based psychological practice in correctional settings

Evidence-Based Practice (EBP) represents the gold standard for effective clinical psychological practice. In this review, we examine ways in which EBP tenets are being neglected by correctional psychologists worldwide. We examine three key aspects of EBP currently being neglected: (a) individualized and flexible client focus, (b) the therapeutic alliance, and (c) psychological expertise. We also highlight two highly related issues responsible for correctional psychologists' neglect of EBP. The first relates to policy makers' and correctional psychologists' overreliance on the Risk–Need–Responsivity Model to guide correctional practice. We argue that the narrow focus and implementation of this model has resulted in a severe identity problem for correctional psychologists that has severely exacerbated the dual relationship problem. That is, the tension psychologists experience as a result of engaging in psychological practice while also obliging the risk and security policies of correctional systems. The second issue concerns psychologists' response to the dual relationship problem. In short, psychology, as a discipline appears to have acquiesced to the dual-relationship problem. In our view, this constitutes a ‘crisis’ for the discipline of correctional psychology. We offer several recommendations for injecting EBP back into correctional psychology for the individual, psychology as a discipline, and correctional policy makers

MammoApplet: an interactive Java applet tool for manual annotation in medical imaging

Web-based applications in computational medicine have become increasingly important during the last years. The rapid growth of the World Wide Web supposes a new paradigm in the telemedicine and eHealth areas in order to assist and enhance the prevention, diagnosis and treatment of patients. Furthermore, training of radiologists and management of medical databases are also becoming increasingly important issues in the field. In this paper, we present MammoApplet , an interactive Java applet interface designed as a web-based tool. It aims to facilitate the diagnosis of new mammographic cases by providing a set of image processing tools that allow a better visualization of the images, and a set of drawing tools, used to annotate the suspicious regions. Each annotation allows including the attributes considered by the experts when issuing the final diagnosis. The overall set of overlays is stored in a database as XML files associated with the original images. The final goal is to obtain a database of already diagnosed cases for training and enhancing the performance of novice radiologistsPeer ReviewedPostprint (author's final draft

Reinforcement learning for efficient network penetration testing

Penetration testing (also known as pentesting or PT) is a common practice for actively assessing the defenses of a computer network by planning and executing all possible attacks to discover and exploit existing vulnerabilities. Current penetration testing methods are increasingly becoming non-standard, composite and resource-consuming despite the use of evolving tools. In this paper, we propose and evaluate an AI-based pentesting system which makes use of machine learning techniques, namely reinforcement learning (RL) to learn and reproduce average and complex pentesting activities. The proposed system is named Intelligent Automated Penetration Testing System (IAPTS) consisting of a module that integrates with industrial PT frameworks to enable them to capture information, learn from experience, and reproduce tests in future similar testing cases. IAPTS aims to save human resources while producing much-enhanced results in terms of time consumption, reliability and frequency of testing. IAPTS takes the approach of modeling PT environments and tasks as a partially observed Markov decision process (POMDP) problem which is solved by POMDP-solver. Although the scope of this paper is limited to network infrastructures PT planning and not the entire practice, the obtained results support the hypothesis that RL can enhance PT beyond the capabilities of any human PT expert in terms of time consumed, covered attacking vectors, accuracy and reliability of the outputs. In addition, this work tackles the complex problem of expertise capturing and re-use by allowing the IAPTS learning module to store and re-use PT policies in the same way that a human PT expert would learn but in a more efficient way

Capturing Ambiguity in Crowdsourcing Frame Disambiguation

FrameNet is a computational linguistics resource composed of semantic frames, high-level concepts that represent the meanings of words. In this paper, we present an approach to gather frame disambiguation annotations in sentences using a crowdsourcing approach with multiple workers per sentence to capture inter-annotator disagreement. We perform an experiment over a set of 433 sentences annotated with frames from the FrameNet corpus, and show that the aggregated crowd annotations achieve an F1 score greater than 0.67 as compared to expert linguists. We highlight cases where the crowd annotation was correct even though the expert is in disagreement, arguing for the need to have multiple annotators per sentence. Most importantly, we examine cases in which crowd workers could not agree, and demonstrate that these cases exhibit ambiguity, either in the sentence, frame, or the task itself, and argue that collapsing such cases to a single, discrete truth value (i.e. correct or incorrect) is inappropriate, creating arbitrary targets for machine learning.Comment: in publication at the sixth AAAI Conference on Human Computation and Crowdsourcing (HCOMP) 201