An analysis of software aging in cloud environment

Cloud Computing is the environment in which several virtual machines (VM) run concurrently on physical machines. The cloud computing infrastructure hosts multiple cloud service segments that communicate with each other using the interfaces. This creates distributed computing environment. During operation, the software systems accumulate errors or garbage that leads to system failure and other hazardous consequences. This status is called software aging. Software aging happens because of memory fragmentation, resource consumption in large scale and accumulation of numerical error. Software aging degrads the performance that may result in system failure. This happens because of premature resource exhaustion. This issue cannot be determined during software testing phase because of the dynamic nature of operation. The errors that cause software aging are of special types. These errors do not disturb the software functionality but target the response time and its environment. This issue is to be resolved only during run time as it occurs because of the dynamic nature of the problem. To alleviate the impact of software aging, software rejuvenation technique is being used. Rejuvenation process reboots the system or re-initiates the softwares. This avoids faults or failure. Software rejuvenation removes accumulated error conditions, frees up deadlocks and defragments operating system resources like memory. Hence, it avoids future failures of system that may happen due to software aging. As service availability is crucial, software rejuvenation is to be carried out at defined schedules without disrupting the service. The presence of Software rejuvenation techniques can make software systems more trustworthy. Software designers are using this concept to improve the quality and reliability of the software. Software aging and rejuvenation has generated a lot of research interest in recent years. This work reviews some of the research works related to detection of software aging and identifies research gaps

An efficient approach for secured communication in wireless sensor networks

Wireless sensor network (WSN) have limited bandwidth, low computational functions, energy constraints. Inspite of these constraints, WSN is useful where communication happens without infrastructure support. The main concern of WSN is the security as the sensor nodes may be attacked and information may be hacked. Security of WSN should have the capability to ensure that the message received was sent by the particular sent node and not modified during transmission. WSN applications require lightweight and strong authentication mechanisms for obtaining data from unprivileged users. In wireless sensor networks, authentication is the effective method to stop unauthorized and undisrupted communication service. In order to strengthen the authenticated communication, several researchers have developed mechanisms. Some of the techniques work with identifying the attacked node or detecting injected bogus message in the network. Encryption and decryption are the popular methods of providing the security. These are based on either public-key or symmetric-key cryptosystems Many of the existing solutions have limitations in communication and computational expertise. Also, the existing mechanisms lack in providing strength and scalability of the network. In order address these issues; a polynomial based method was introduced in recent days. Key distribution is a significant aspect in key management in WSNs. The simplest method of distribution of key is by hand which was used in the days of couriers. Now a days, most distribution of keys is done automatically. The automatic distribution of keys is essential and convenient in networks that require two parties to transmit their security keys in the same communication medium. In this work, a new type of key exchange mechanism is proposed. The proposed method for authentication among sensor nodes proves to be promising as per the simulation results. The nodes which are unknown to each other setup a private however arbitrary key for the symmetric key cryptosystem

Software aging prediction – a new approach

To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed

Early Identification of Alzheimer’s Disease Using Medical Imaging: A Review From a Machine Learning Approach Perspective

Alzheimer’s disease (AD) is the leading cause of dementia in aged adults, affecting up to 70% of the dementia patients, and posing a serious public health hazard in the twenty-first century. AD is a progressive, irreversible and neuro-degenerative disease with a long pre-clinical period, affecting brain cells leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very later stage, after irreversible damages to the brain cells have occurred, when there is no scope to prevent further cognitive decline. The use of non-invasive neuroimaging procedures capable of detecting AD at preliminary stages is crucial for providing treatment retarding disease progression, and has stood as a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from Alzheimer’s disease neuroimaging initiative (ADNI) dataset, consisting of magnetic resonance image (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4 percent, whereas convolutional neural networks(CNN) have a mean accuracy of 78.5 percent. Better classification accuracy has been achieved by combining MRI and PET, rather using single neuroimaging technique. Overall, more complicated models, like deep learning, paired with multimodal and multidimensional data (neuroimaging, cognitive, clinical, behavioral and genetic) produced superlative results. However, promising results have been achieved, still there is a room for performance improvement of the proposed methods, providing assistance to healthcare professionals and clinician

Child marriage and maternal health risks among young mothers in Gombi, Adamawa State, Nigeria: implications for mortality, entitlements and freedoms.

Background: Efforts toward liberation of the girl-child from the shackles of early marriage have continued to be resisted through tradition, culture and religion in some parts of Nigeria. Objective: This study therefore examines the maternal health implications of early marriage on young mothers in the study area. Methods: Multistage sampling technique was employed to obtained data from 200 young mothers aged 15-24 years who married before aged 16 years. Findings: The study reveals that more than 60% had only primary education while more than 70% had experienced complications before or after childbirth. Age at first marriage, current age, level of education and household decision-making significantly influence (P<0.005) maternal health risks in the study area. The study establishes that respondents in age group 15-19 years are 1.234 times more likely to experience complications when compared with the reference category 20-24 years. Entitlements and freedom that are highly relevant to reduction of maternal mortality, provided by international treaties are inaccessible to young women in the study area. Conclusion: Strategies to end child marriage in the study area should include mass and compulsory education of girls, provision of other options to early marriage and childbearing and involvement of fathers in preventing and ending the practice

Antioxidant, antiinflammatory and antiinvasive activities of biopolyphenolics

A large number of polyphenolic and heterocyclic compounds, i.e. 4-methylcoumarins, 4-methylthionocoumarins, xanthones, pyrazoles, pyrazolylacrylonitriles, flavones and isoflavones have been tested for their antioxidant activity towards NADPH-catalysed liver-microsomal lipid peroxidation with a view to establish their structure-activity relationship. Inhibition of microsomal lipid peroxidation by 7,8-dihydroxy-4-methylcoumarin (DHMC, 2) and 7,8-diacetoxy-4-methylcoumarin (DAMC, 3) was intriguing. We also found that dihydroxy and diacetoxy derivatives of 4-methylthionocoumarin were more potent in comparison to the corresponding coumarin derivatives in inhibiting TNF-α induced expression of ICAM-1. The effect of nine different xanthones has been examined on the modulation of cytokine-induced expression of ICAM-1 in human endothelial cells. 1,4-Dihydroxyxanthone (10) showed enhanced antioxidant activity as well as the inhibition of the expression of cell adhesion molecules, such as ICAM-1, VCAM-1 and E-selectin on endothelial cells in a concentration and time dependent manner. Antioxidant activity of different pyrazoles and pyrazolylacrylonitriles and antiinvasive activity of flavones and isoflavones against solid tumors have also been studied

Antioxidant, antiinflammatory and antiinvasive activities of biopolyphenolics

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DBGC:Dimension-Based Generic Convolution Block for Object Recognition

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs

Synthesis of macromolecular systems via lipase catalyzed biocatalytic reactions: applications and future perspectives

Enzymes, being remarkable catalysts, are capable of accepting a wide range of complex molecules as substrates and catalyze a variety of reactions with a high degree of chemo-, stereo- and regioselectivity in most of the reactions. Biocatalysis can be used in both simple and complex chemical transformations without the need for tedious protection and deprotection chemistry that is very common in traditional organic synthesis. This current review highlights the applicability of one class of biocatalysts viz. ‘‘lipases’’ in synthetic transformations, the resolution of pharmaceutically important small molecules including polyphenols, amides, nucleosides and their precursors, the development of macromolecular systems (and their applications as drug/gene carriers), flame retardants, polymeric antioxidants and nanocrystalline solar cells, etc