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Optimizing the Time complexity of any program is still the most researched and sought area for researchers. At the industry level, the Software execution timing is the dominant criteria for Workload selection. One prominent method for reducing the Time complexity of a program is by using program slicing configuration, without affecting the program flow. Program slicing is the process of slicing a program in such a way that it reduces the time of debugging. This paper presents a timed-based analysis of a program with and without slicing with the help of different verification tools, namely KLEE, DIVINE, and Frama-C. This paper aims to compare these tools based on the timing of debugging and validity of a program before and after slicing.

With the increase in the number of open-source and commercial code in the market, copyright and license infringement cases are on the rise. The lack of availability of source code makes identification a difficult task as existing techniques heavily rely on the source code. We propose two similarity measurement methods using the instruction log and the call-trace of each executable using Intel Pin tool. A Software Plagiarism Detector(SPD) was developed using the Intel Pin instrumentation tool and we have tested this approach on different small executable single-threaded and multi-threaded files. The results portray the validity of this method. We also talk about the possibility to expand this method for bigger software.

Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial applications such as product reviews and feedback, marketing analysis, etc. The purpose of this work is to analyzes the performance of three classifiers(SVM, Naive Bayes, and Logistic Regression) with respect to providing positive or negative sentiment for three different scenarios(Movie Reviews, Election Opinions, and Food Reviews). The three classifiers are compared using fixed set of preprocessing steps and four different weighting schemes(Term frequency inverse document frequency (TFIDF), Term frequency inverse class frequency (TFICF), Mutual Information (MI), and X2 statistic (CHI)). The controlled experimental results showed that Logistic Regression classifier performs better in terms of overall accuracy when MI is used as weighting scheme.

Efficient performance of a disk I/O operation involves a multitude of factors such as the type of the disk, I/O scheduling, and the type of the file system used. Due to the various types of file systems available, with each having different structure and logic, properties of speed, flexibility, security, size and more, it becomes imperative to have an objective overview of the merits and demerits of each file system according to the needs of the users. In this work, we present a thorough performance evaluation of ext4, NTFS and Btrfs filesystems along with CFQ, NOOP and Deadline I/O schedulers tested on regular hard disk drives and SSDs.

Convolution Neural Networks (CNN) models have shown remarkable results for classifying text and sentiments. In this paper, we present our approach on the task of classifying movie reviews using the word embedding on a large-scale dataset provided by Stanford. We compare the accuracy of word-based CNN models for different kernel sizes with structured and unstructured sentences. We use the standard GLoVe for word embedding and use MXNet framework to perform our experiments. Our results suggest that word orderings in a sentence do not affect the classification accuracy of CNN models significantly. However, we also note that accuracy may depend on frequency of occurrences of words.

Performance degradation is unavoidable in server systems and this is because of factors suchas shrinkage of system resources, data corruption, and numerical error accumulation. The resource shrinkageleads to the system failure due to the error propagation. Thus the resource prediction is useful to theadministrator of the system so that an accidental outage can be avoided. It has been observed in past thatmost of the failures occur due to the exhaustion of free physical memory, so here free physical memory of aserver consolidation setup is observed. It is also found that most of the studies in this direction were using themeasurement-based approach with time series models for prediction. This paper reviews the effectiveness ofsuch models and it examines whether volatility is present in the data or not. It checks whether Gauss-Markovassumptions about homoscedasticity holds good for the ordinary least square estimators of such models ornot. This paper applies a combination of AutoRegressive Integrated Moving Average - AutoRegressiveConditional Heteroskedastic (ARIMA-ARCH) model to predict resource usage. Experimental resultsdemonstrate that the goodness of fit of the ARIMA-ARCH Model has improved when compared to the linearARIMA model.

This paper uses a case based study – “product sales estimation” on real-time data to help us understand the applicability of linear and non-linear models in machine learning and data mining. A systematic approach has been used here to address the given problem statement of sales estimation for a particular set of products in multiple categories by applying both linear and non-linear machine learning techniques on a data set of selected features from the original data set. Feature selection is a process that reduces the dimensionality of the data set by excluding those features which contribute minimal to the prediction of the dependent variable. The next step in this process is training the model that is done using multiple techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling has then been done to extract new features from the data set by changing the structure of the dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and important role in boosting classifier accuracies by changing the properties of the given dataset. We then try to explore and analyze the various reasons due to which one model performs better than the other & hence try and develop an understanding about the applicability of linear & non-linear machine learning models. The target mentioned above being our primary goal, we also aim to find the classifier with the best possible accuracy for product sales estimation in the given scenario.

The paper aims to determine the forest cover type of the dataset containing cartographic attributes evaluated over four wilderness areas of Roosevelt National Forest of Northern Colorado. The cover type data is provided by US Forest service inventory, while Geographic Information System (GIS) was used to derive cartographic attributes like elevation, slope, soil type etc. Dataset was analyzed, pre processed and feature engineering techniques were applied to derive relevant and non-redundant features. A comparative study of various decision tree algorithms namely, CART, C4.5, C5.0 was performed on the dataset. With the new dataset built by applying feature engineering techniques, Random Forest and C5.0 improved the accuracy by 9% compared to the raw dataset.

Nowadays virtualization playing a vital role in the cloud technology. Users deploying websites on virtual private servers called instances which is cost effective and scalable. The explosive growth in World Wide Web, users are able to access things with any device at any time and any place. Increasing the access will constitutes the load and stress on the servers. Sometrics are useful to gauge the performance of various architectures. In this analysis we consider three matrices, they are responsiveness, scalability and efficiency. We have defined response time, memory usage and error rate as key factors for responsiveness, scalability and efficiency respectively. In this paper we investigate the performance of two web servers in different scenario, i.e. Apache - process-based webserver and Nginx - asymmetric multi-process event-driven architecture (AMPED). Our research shows that the Nginx server out performs Apache server in terms of responsiveness and scalability, while Apache ensures the efficiency. The results are useful for those who want to host a website on virtual private server (VPS).

Degradation of the performance is the part of any long running software systems. This is due to memory leakage, unreleased file descriptors, round off errors and disk and memory fragmentation. It has been found that the memory leakage is the primary cause of any software performance degradation. In order to predict the software performance degradation, the analysis of the resource usage is essential. Here the free physical memory of a server virtualised system is analysed using time series analysis.

This paper uses a case based study – “product sales estimation” on real-time data to understand the applicability of linear and non-linear models. We use a systematic approach to address the given problem statement of sales estimation for a given product by applying both linear and non-linear techniques on a data set of selected features from the original data set. Feature selection is a process that reduces the dimensionality of the data set by eliminating those features which contribute minimal to the prediction of the dependent variable. The next step is training the model which is done using two techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Re-modeling has then been done to extract new features from the data set by changing the structure of the dataset & the performance of the models is checked again. Data Remodeling often plays a crucial role in boosting classifier accuracies by changing the properties of the dataset. We then try to analyze the reasons due to which one model proves to be better than the other & hence try and develop an understanding about the applicability of linear & non-linear models. The target mentioned above being our primary goal, we also aim to find the classifier with the best possible accuracy for product sales estimation in the given scenario.

It is well known that software systems suffer from performance degradation due to resource shrinking and this phenomenon is referred to as Software Aging. Long running software systems tend to show degradation in performance due to exhaustion of operating systems resources, data corruption and numerical error accumulation. The primary objective of the paper is to establish the aging trend in the server virtualized system. It establishes the aging trend by showing that the average response time decreases while total available physical memory decreases. Linear regression model has been used to study the aging trend.

Virtualization is rapidly gaining acceptance as a fundamental building block in enterprise data centers and is the core component of cloud computing platforms. It is most known for improving efficiency and ease of management. While this technology is meant to enhance the security of computer systems, some recent attacks show that virtual machine technology has much vulnerability and becomes exposed to security threats. In this paper, we focus on Vulnerability Analysis on Virtualized Environment using First Principles Vulnerability Analysis (FPVA) methodology. This paper analyses the security of Interrupt Descriptor Table on Xen hypervisor and presents outcome of analysis. This paper aids other enthusiasts in better analysing the Security aspects of Xen system using their own programs for security vulnerabilities.

In this paper a novel theoretical model is presented in which the dynamics in a knowledge-based value chain is modeled using Petri nets. From the generic scheme of a knowledge-based value chain, the various components are individually modeled. The theory of Petri nets aptly captures the evolution of knowledge in a system and the process is usually highly interactive in nature. The properties and analysis methods of various classes of Petri nets can be conveniently used to check various constraints while designing the system.