Data Mining Practical Machine Learning Tools and Techniques

Social Network Analysis

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Series Editor's Introduction Preface Acknowledgments 1. The Idea of Agent-Based Modeling 1.1 Agent-Based Modeling 1.2 Some Examples 1.3 The Features of Agent-Based Modeling 1.4 Other Related Modeling Approaches 2. Agents, Environments, and Timescales 2.1 Agents 2.2 Environments 2.3 Randomness 2.4 Time 3. Using Agent-Based Models in Social Science Research 3.1 An Example of Developing an Agent-Based Model 3.2 Verification: Getting Rid of the Bugs 3.3 Validation 3.4 Techniques for Validation 3.5 Summary 4. Designing and Developing Agent-Based Models 4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments 4.2 Using NetLogo to Build Models 4.3 Building the Collectivities Model Step by Step 4.4 Planning an Agent-Based Model Project 4.5 Reporting Agent-Based Model Research 4.6 Summary 5. Advances in Agent-Based Modeling 5.1 Geographical Information Systems 5.2 Learning 5.3 Simulating Language Resources Glossary References Index About the Author

What are agent-based models?

Today is the era of the Internet of Things (IoT). The recent advances in hardware and information technology have accelerated the deployment of billions of interconnected, smart and adaptive devices in critical infrastructures like health, transportation, environmental control, and home automation. Transferring data over a network without requiring any kind of human-to-computer or human-to-human interaction, brings reliability and convenience to consumers, but also opens a new world of opportunity for intruders, and introduces a whole set of unique and complicated questions to the field of Digital Forensics. Although IoT data could be a rich source of evidence, forensics professionals cope with diverse problems, starting from the huge variety of IoT devices and non-standard formats, to the multi-tenant cloud infrastructure and the resulting multi-jurisdictional litigations. A further challenge is the end-to-end encryption which represents a trade-off between users’ right to privacy and the success of the forensics investigation. Due to its volatile nature, digital evidence has to be acquired and analyzed using validated tools and techniques that ensure the maintenance of the Chain of Custody. Therefore, the purpose of this paper is to identify and discuss the main issues involved in the complex process of IoT-based investigations, particularly all legal, privacy and cloud security challenges. Furthermore, this work provides an overview of the past and current theoretical models in the digital forensics science. Special attention is paid to frameworks that aim to extract data in a privacy-preserving manner or secure the evidence integrity using decentralized blockchain-based solutions. In addition, the present paper addresses the ongoing Forensics-as-a-Service (FaaS) paradigm, as well as some promising cross-cutting data reduction and forensics intelligence techniques. Finally, several other research trends and open issues are presented, with emphasis on the need for proactive Forensics Readiness strategies and generally agreed-upon standards.

/pdf/a-survey-on-the-internet-of-things-iot-forensics-challenges-3w6jzejgc2.pdf

A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues

Due to the growing number of articles published every year as the research output, it is imperative to analyze their impact on future and current research. In this cito-analytical study, we use two publicly available citation datasets, i.e., arXiv’s High-Energy Physics Citation Theory Network and Cora Citation Network. This study employs different macro-meso-micro level indicators such as K-cores, centrality measures, and clustering coefficient in identifying relevant network characteristics and establishing their inter-relationships to determine impactful research. While the meso-level feature identifies the type of citation network, the micro-level indicators (centrality measures) help in recognizing the individual node (research paper) strength and macro-level statistics comments upon the global network characteristics. The current exposition empirically demonstrates the relevance of using macro-meso-micro level statistics together as the unit in determining influential and significant research output. While previous researchers have independently used these metrics in other academic networks, we, however, showed their importance and inter-relationship using an integrationist approach in citation networks.

An Empirical Study on the Role of Macro-Meso-Micro Measures in Citation Networks

This study examines the need to consider interactions between the measurements (metrics) of different quality factors in the development of software quality models. Though the correlation between metrics has been explored to a considerable depth in the development of these models, consideration of interactions between predictors is comparatively new in software engineering. This preliminary study is supported by statistically-proven results, differentiating interactions with correlation analysis.The issues raised here will assist analysts to improve empirical analyses by incorporating interactions in software quality model development, where amalgamating effects between different characteristics or subcharacteristics are observed.

Why interaction between metrics should be considered in the development of software quality models: a preliminary study

In this paper we describe our efforts to design and implement an agent development framework that has the potential to scale to the size of any underlying network suitable for various ECommerce activities. The main novelty in our framework is it’s capability to allow the development of sophisticated, secured agents which are simple enough to be practical. We have adopted FIPA agent platform reference Model as backbone for implementation along with XML for agent Communication and Java Cryptographic Extension and architecture to realize the security of communication information between agents. The advantage of our architecture is its support of agents development in different languages and Communicating with each other using a more open standard i.e. XML Keywords— Agent, Agent Development Framework, Agent Coordination, Security

XML based Safe and Scalable Multi-Agent Development Framework

The presence of bad smells in code hampers software’s maintainability, comprehensibility, and extensibility. A type of code smell, which is common in software projects, is “duplicated code” bad smell, also known as code clones. These types of smells generally arise in a software system due to the copy-paste-modify actions of software developers. They can either be exact copies or copies with certain modifications. Different clone detection techniques exist, which can be broadly classified as text-based, token-based, abstract syntax tree-based (AST-based), metrics-based, or program dependence graph-based (PDG-based) approaches based on the amount of preprocessing required on the input source code. Researchers have also built clone detection techniques using a hybrid of two or more approaches described above. In this paper, we did a narrative review of the metrics-based techniques (solo or hybrid) reported in the previously published studies and analyzed them for their quality in terms of run-time efficiency, accuracy values, and the types of clones they detect. This study can be helpful for practitioners to select an appropriate set of metrics, measuring all the code characteristics required for clone detection in a particular scenario. 

A Study on the Metrics-Based Duplicated Code Type Smell Detection Techniques Relating the Metrics to Its Quality

Software clones are code fragments with similar or nearly similar functionality or structures. These clones are introduced in a project either accidentally or deliberately during software development or maintenance process. The presence of clones poses a significant threat to the maintenance of software systems and is on the top of the list of code smell types. Clones can be simple (fine-grained) or high-level (coarse-grained), depending on the chosen granularity of code for the clone detection. Simple clones are generally viewed at the lines/statements level, whereas high-level clones have granularity as a block, method, class, or file. High-level clones are said to be composed of multiple simple clones. This study aims to detect high-level conceptual code clones (having granularity as java methods) in java-based projects, which is extendable to the projects developed in other languages as well. Conceptual code clones are the ones implementing a similar higher-level abstraction such as an Abstract Data Type (ADT) list. Based on the assumption that “similar documentation implies similar methods”, the proposed mechanism uses “documentation” associated with methods to identify method-level concept clones. As complete documentation does not contribute to the method’s semantics, we extracted only the description part of the method’s documentation, which led to two benefits: increased efficiency and reduced text corpus size. Further, we used Latent Semantic Indexing (LSI) with different combinations of weight and similarity measures to identify similar descriptions in the text corpus. To show the efficacy of the proposed approach, we validated it using three java open source systems of sufficient length. The findings suggest that the proposed mechanism can detect methods implementing similar high-level concepts with improved recall values.

https://www.mdpi.com/2073-8994/13/3/447/pdf

Rinkaj Goyal

Papers

An Empirical Study on the Role of Macro-Meso-Micro Measures in Citation Networks

Why interaction between metrics should be considered in the development of software quality models: a preliminary study

XML based Safe and Scalable Multi-Agent Development Framework

A Study on the Metrics-Based Duplicated Code Type Smell Detection Techniques Relating the Metrics to Its Quality

Identifying High-Level Concept Clones in Software Programs Using Method’s Descriptive Documentation