Logistic Map-Based Fragile Watermarking for Pixel Level Tamper Detection and Resistance

An efficient fragile image watermarking technique for pixel level tamper detection and resistance is proposed. It uses five most significant bits of the pixels to generate watermark bits and embeds them in the three least significant bits. The proposed technique uses a logistic map and takes advantage of its sensitivity property to a small change in the initial condition. At the same time, it incorporates the confusion/diffusion and hashing techniques used in many cryptographic systems to resist tampering at pixel level as well as at block level. This paper also presents two new approaches called nonaggressive and aggressive tamper detection algorithms. Simulations show that the proposed technique can provide more than 99.39% tamper detection capability with less than 2.31% false-positive detection and less than 0.61% false-negative detection responses

A Software Engineering Schema for Data Intensive Applications

The features developed by a software engineer (system specification) for a software system may significantly differ from the features required by a user (user requirements) for their envisioned system. These discrepancies are generally resulted from the complexity of the system, the vagueness of the user requirements, or the lack of knowledge and experience of the software engineer. The principles of software engineering and the recommendations of the ACM's Software Engineering Education Knowledge (SEEK) document can provide solutions to minimize these discrepancies; in turn, improve the quality of a software system and increase user satisfaction. In this paper, a software development framework, called SETh, is presented. The SETh framework consists of a set of visual models that support software engineering education and practices in a systematic manner. It also enables backward tracking/tracing and forward tracking/tracing capabilities - two important concepts that can facilitate the greenfield and evolutionary type software engineering projects. The SETh framework connects every step of the development of a software system tightly; hence, the learners and the experienced software engineers can study, understand, and build efficient software systems for emerging data science applications

Optimization: A Journal of Mathematical Programming and Operations Research

In this article we study support vector machine (SVM) classifiers in the face of uncertain knowledge sets and show how data uncertainty in knowledge sets can be treated in SVM classification by employing robust optimization. We present knowledge-based SVM classifiers with uncertain knowledge sets using convex quadratic optimization duality. We show that the knowledge-based SVM, where prior knowledge is in the form of uncertain linear constraints, results in an uncertain convex optimization problem with a set containment constraint. Using a new extension of Farkas' lemma, we reformulate the robust counterpart of the uncertain convex optimization problem in the case of interval uncertainty as a convex quadratic optimization problem. We then reformulate the resulting convex optimization problems as a simple quadratic optimization problem with non-negativity constraints using the Lagrange duality. We obtain the solution of the converted problem by a fixed point iterative algorithm and establish the convergence of the algorithm. We finally present some preliminary results of our computational experiments of the metho

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. An ignorance of observing the progression of this fast-growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics

Big Data Classification: Problems and Challenges in Network Intrusion Prediction with Machine Learning

This paper focuses on the specific problem of Big Data classification of network intrusion traffic. It discusses the system challenges presented by the Big Data problems associated with network intrusion prediction. The prediction of a possible intrusion attack in a network requires continuous collection of traffic data and learning of their characteristics on the fly. The continuous collection of traffic data by the network leads to Big Data problems that are caused by the volume, variety and velocity properties of Big Data. The learning of the network characteristics requires machine learning techniques that capture global knowledge of the traffic patterns. The Big Data properties will lead to significant system challenges to implement machine learning frameworks. This paper discusses the problems and challenges in handling Big Data classification using geometric representation-learning techniques and the modern Big Data networking technologies. In particular this paper discusses the issues related to combining supervised learning techniques, representation-learning techniques, machine lifelong learning techniques and Big Data technologies (e.g. Hadoop, Hive and Cloud) for solving network traffic classification problems

FLaSKU - A classroom experience with teaching computer networking: Is it useful to others in the field?

In general, every educator has a classroom experience that he or she wants to share for the benefit of other educators and students in the field. This paper presents a classroom experience with teaching a computer networking course to both undergraduate and graduate students in Information Technology (IT) areas. This course uses conceptualization and summarization techniques coupled with standard teaching methods, such as independent learning, incremental learning, and out-of-class assignments. It also defines two terms: independent conceptualization and dependent conceptualization, and adopts them with a summarization technique to improve conceptualized computer networking. Two simple examples are presented to illustrate these definitions. The paper also presents a teaching philosophy and a flexible grading policy that help motivate learning over earning a grade. The experience and knowledge gained from the delivery of a computer networking course over eight years is shared in this paper. Course evaluations were conducted using a departmental questionnaire, peer evaluations, and an independent survey. The course evaluation results of over eight years demonstrate a significant improvement in the overall quality of the course delivery. The methods, results, and findings can deliver benefits to young university educators and students in the IT field

No-reference visually significant blocking artifact metric for natural scene images

Quantifying visually annoying blocking artifacts is essential for image and video quality assessment. This paper presents a no-reference technique that uses the multi neural channels aspect of human visual system (HVS) to quantify visual impairment by altering the outputs of these sensory channels independently using statistical “standard score” formula in the Fourier domain. It also uses the bit patterns of the least significant bits (LSB) to extract blocking artifacts. Simulation results show that the blocking artifact extracted using this approach follows subjective visual interpretation of blocking artifacts. This paper also presents a visually significant blocking artifact metric (VSBAM) along with some experimental results

Characterization of Differentially Private Logistic Regression

The purpose of this paper is to present an approach that can help data owners select suitable values for the privacy parameter of a differentially private logistic regression (DPLR), whose main intention is to achieve a balance between privacy strength and classification accuracy. The proposed approach implements a supervised learning technique and a feature extraction technique to address this challenging problem and generate solutions. The supervised learning technique selects subspaces from a training data set and generates DPLR classifiers for a range of values of the privacy parameter. The feature extraction technique transforms an original subspace to a differentially private subspace by querying the original subspace multiple times using the DPLR model and the privacy parameter values that were selected by the supervised learning module. The proposed approach then employs a signal processing technique called signal-interference-ratio as a measure to quantify the privacy level of the differentially private subspaces; hence, allows data owner learn the privacy level that the DPLR models can provide for a given subspace and a given classification accuracy

Modeling of class imbalance using an empirical approach with spambase dataset and random forest classification

Classification of imbalanced data is an important research problem as most of the data encountered in real world systems is imbalanced. Recently a representation learning technique called Synthetic Minority Over-sampling Technique (SMOTE) has been proposed to handle imbalanced data problem. Random Forest (RF) algorithm with SMOTE has been previously used to improve classification performance in minority class over majority class. Although RF with SMOTE demonstrates improved classification performance, the relationship between the classification performance and the imbalanced ratio between the majority and minority classes is not well defined. Therefore mathematical models that describe this relationship is useful especially in the big data environment which suffers from imbalanced data. In this paper, we proposed a mathematical model using an empirical approach applied to the well known Spambase dataset and Random Forest classification approach including its adoption with SMOTE representation learning technique. We have presented a linear model which describes the relationship between true positive classification rate and the imbalanced ratio between the majority and minority classes. This model can help IT researchers to develop better spam filter algorithms

Reduction of queue oscillation in the next generation Internet routers

The Internet routers employing the random early detection (RED) algorithm for congestion control suffer from the problem of chaotic queue oscillation. It is well known that the slowly varying nature of the average queue size computed using an exponentially weighted moving average (EWMA) used in the RED scheme causes this chaotic behavior. This paper presents a new mathematical function to model the weighting parameter used in the EWMA. The proposed weighting function incorporates the knowledge of the dynamic changes in the congestion characteristics, traffic characteristics and queue normalization. Using this pragmatic information eliminates the slowly varying nature of the average queue size. It is evident from our simulations that the proposed approach not only reduces the chaotic queue oscillation significantly but also provides predictable low delay and low delay jitter with high throughput gain and reduced packet loss rate even under heavy load of traffic conditions