Composing RESTful Services and Collaborative Workflows: A Lightweight Approach

The use of RESTful Web services has gained momentum in the development of distributed applications based on traditional Web standards such as HTTP. In particular, these services can integrate easily into various applications, such as mashups. Composing RESTful services into Web-scale workflows requires a lightweight composition language that's capable of describing both the control and data flow that constitute a workflow. The authors address these issues with Bite, a lightweight and extensible composition language that enables the creation of Web-scale workflows and uses RESTful services as its main composable entities.     

Religion and spirituality: Linkages to physical health.

Evidence is presented that bears on 9 hypotheses about the link between religion or spirituality and mortality, morbidity, disability, or recovery from illness. In healthy participants, there is a strong, consistent, prospective, and often graded reduction in risk of mortality in church/service attenders. This reduction is approximately 25% after adjustment for confounders. Religion or spirituality protects against cardiovascular disease, largely mediated by the healthy lifestyle it encourages. Evidence fails to support a link between depth of religiousness and physical health. In patients, there are consistent failures to support the hypotheses that religion or spirituality slows the progression of cancer or improves recovery from acute illness but some evidence that religion or spirituality impedes recovery from acute illness. The authors conclude that church/service attendance protects healthy people against death. More methodologically sound studies are needed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Enhancing the precision of the Mississippi Scale for Combat-Related Posttraumatic Stress Disorder: An application of item response theory.

Examined, using item response theory, the measurement qualities of the Mississippi Scale for Combat-Related Posttraumatic Stress Disorder, with data taken from the 2,348 veteran participants in the National Vietnam Veterans Readjustment Study. Using F. Samejima's (1969) graded response model, estimates of each item's discrimination and difficulty parameters were derived, and item and test information functions were then computed. Various item information patterns and sample items are discussed in terms of improved assessment of posttraumatic stress disorder (PTSD). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Optimal Artificial Intelligence Based Automated Skin Lesion Detection and Classification Model

Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish malignant skin lesions from benign lesions in dermoscopic images. This study presents an automated skin lesion detection and classification technique utilizing optimized stacked sparse autoencoder (OSSAE) based feature extractor with backpropagation neural network (BPNN), named the OSSAE-BPNN technique. The proposed technique contains a multi-level thresholding based segmentation technique for detecting the affected lesion region. In addition, the OSSAE based feature extractor and BPNN based classifier are employed for skin lesion diagnosis. Moreover, the parameter tuning of the SSAE model is carried out by the use of sea gull optimization (SGO) algorithm. To showcase the enhanced outcomes of the OSSAE-BPNN model, a comprehensive experimental analysis is performed on the benchmark dataset. The experimental findings demonstrated that the OSSAE-BPNN approach outperformed other current strategies in terms of several assessment metrics.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

Admission control mechanism and performance analysis based on stochastic automata networks formalism

IEEE 802.16 has been designed to support QoS (Quality of Service) in Wireless broadband Metropolitan Networks (WMAN), and specifically in the access networks. To achieve this, the IEEE 802.16.e amendment introduces the service differentiation by five service classes (UGS, ertPS, rtPS, nrtPS and BE). To maintain the QoS of active connections and to avoid any congestion in the network, an Admission Control (AC) is defined. AC aims to accept or reject a new connection according to the negotiated parameters and the availability of resources in the network. This mechanism is not standardized and let to the operators. We tackle this point, by proposing in this study a new Admission Control (AC) that improves the QoS of BE traffic by avoiding a strict bandwidth assignment of other traffics (rtPS and nrtPS) as is defined in major previous studies. The proposed mechanism is based on token bucket for rtPS, nrtPS and BE traffics in order to reduce lightly the number of accepted connections and to improve considerably the number of accepted BE connections. To evaluate the performance of the proposed scheme, we use PEPS (Performance Evaluation of Parallel Systems) which is a powerful tool based on models which can be described with Stochastic Automata Networks (SAN). PEPS can solve complex models with a large state space and with many synchronized events. Therefore, we show that this tool is suitable for wireless network, and specifically for the proposed scheme, toward numerical results we show as we expected that our proposed AC outperform the classical one by reducing lightly the performance of other service classes.     

High-aspect ratio metal tips attached to atomic force microscopy cantilevers with controlled angle, length, and radius for electrostatic force microscopy

We demonstrate a method to fabricate a high-aspect ratio metal tip attached to microfabricated cantilevers with controlled angle, length, and radius, for use in electrostatic force microscopy. A metal wire, after gluing it into a guiding slot that is cut into the cantilever, is shaped into a long, thin tip using a focused ion beam. The high-aspect ratio results in considerable reduction of the capacitive force between tip body and sample when compared to a metal coated pyramidal tip.     

Risk Factors for Partner Violence Among a National Sample of Combat Veterans.

In this study, the authors identified potential risk factors for partner violence perpetration among a subsample (n=109) of men who participated in a national study of Vietnam veterans. Partner violent (PV) men with posttraumatic stress disorder (PTSD) were compared with PV men without PTSD and nonviolent men with PTSD on family-of-origin variables, psychiatric problems, relationship problems, and war-zone factors. PV men with PTSD were the highest of the 3 groups on every risk factor other than childhood abuse. Group contrasts and a classification tree analysis suggest some potential markers and mechanisms for the association between PTSD and partner violence among military veterans and highlight the need for theory development in this area of inquiry. (PsycINFO Database Record (c) 2010 APA, all rights reserved)