Analysis of Social Network Data Mining for Security Intelligence Privacy Machine Learning

The Modern communication on the Internet platform is most responsive through social media. Social media has changed and is still reshaping how we share our thoughts and emotions in communication. It has introduced a constant real-time communication pattern that was before unheard of. Young and old, organizations, governmental agencies, professional associations, etc., all have social media accounts that they use exclusively for communication with other users. Social media also acts as a powerful network engine that connects users regardless of where they are in the world. The development of global communication will greatly benefit from the availability of this new communication platform in the future. Consequently, there is a pressing need to research usage trends. Therefore, it is vital to investigate social media platform usage trends in order to develop automated systems that intelligence services can use to help avert national security incidents. Through the use of social media data mining, this research study suggests an automated machine learning model that can improve speedy response to crises involving national and International security

Similarity search and data mining techniques for advanced database systems.

Modern automated methods for measurement, collection, and analysis of data in industry and science are providing more and more data with drastically increasing structure complexity. On the one hand, this growing complexity is justified by the need for a richer and more precise description of real-world objects, on the other hand it is justified by the rapid progress in measurement and analysis techniques that allow the user a versatile exploration of objects. In order to manage the huge volume of such complex data, advanced database systems are employed. In contrast to conventional database systems that support exact match queries, the user of these advanced database systems focuses on applying similarity search and data mining techniques. Based on an analysis of typical advanced database systems — such as biometrical, biological, multimedia, moving, and CAD-object database systems — the following three challenging characteristics of complexity are detected: uncertainty (probabilistic feature vectors), multiple instances (a set of homogeneous feature vectors), and multiple representations (a set of heterogeneous feature vectors). Therefore, the goal of this thesis is to develop similarity search and data mining techniques that are capable of handling uncertain, multi-instance, and multi-represented objects. The first part of this thesis deals with similarity search techniques. Object identification is a similarity search technique that is typically used for the recognition of objects from image, video, or audio data. Thus, we develop a novel probabilistic model for object identification. Based on it, two novel types of identification queries are defined. In order to process the novel query types efficiently, we introduce an index structure called Gauss-tree. In addition, we specify further probabilistic models and query types for uncertain multi-instance objects and uncertain spatial objects. Based on the index structure, we develop algorithms for an efficient processing of these query types. Practical benefits of using probabilistic feature vectors are demonstrated on a real-world application for video similarity search. Furthermore, a similarity search technique is presented that is based on aggregated multi-instance objects, and that is suitable for video similarity search. This technique takes multiple representations into account in order to achieve better effectiveness. The second part of this thesis deals with two major data mining techniques: clustering and classification. Since privacy preservation is a very important demand of distributed advanced applications, we propose using uncertainty for data obfuscation in order to provide privacy preservation during clustering. Furthermore, a model-based and a density-based clustering method for multi-instance objects are developed. Afterwards, original extensions and enhancements of the density-based clustering algorithms DBSCAN and OPTICS for handling multi-represented objects are introduced. Since several advanced database systems like biological or multimedia database systems handle predefined, very large class systems, two novel classification techniques for large class sets that benefit from using multiple representations are defined. The first classification method is based on the idea of a k-nearest-neighbor classifier. It employs a novel density-based technique to reduce training instances and exploits the entropy impurity of the local neighborhood in order to weight a given representation. The second technique addresses hierarchically-organized class systems. It uses a novel hierarchical, supervised method for the reduction of large multi-instance objects, e.g. audio or video, and applies support vector machines for efficient hierarchical classification of multi-represented objects. User benefits of this technique are demonstrated by a prototype that performs a classification of large music collections. The effectiveness and efficiency of all proposed techniques are discussed and verified by comparison with conventional approaches in versatile experimental evaluations on real-world datasets

Privacy-preserving techniques for computer and network forensics

Clients, administrators, and law enforcement personnel have many privacy concerns when it comes to network forensics. Clients would like to use network services in a freedom-friendly environment that protects their privacy and personal data. Administrators would like to monitor their network, and audit its behavior and functionality for debugging and statistical purposes (which could involve invading the privacy of its network users). Finally, members of law enforcement would like to track and identify any type of digital crimes that occur on the network, and charge the suspects with the appropriate crimes. Members of law enforcement could use some security back doors made available by network administrators, or other forensic tools, that could potentially invade the privacy of network users. In my dissertation, I will be identifying and implementing techniques that each of these entities could use to achieve their goals while preserving the privacy of users on the network. I will show a privacy-preserving implementation of network flow recording that can allow administrators to monitor and audit their network behavior and functionality for debugging and statistical purposes without having this data contain any private information about its users. This implementation is based on identity-based encryption and differential privacy. I will also be showing how law enforcement could use timing channel techniques to fingerprint anonymous servers that are running websites with illegal content and services. Finally I will show the results from a thought experiment about how network administrators can identify pattern-like software that is running on clients\u27 machines remotely without any administrative privileges. The goal of my work is to understand what privileges administrators or law enforcement need to achieve their goals, and the privacy issues inherent in this, and to develop technologies that help administrators and law enforcement achieve their goals while preserving the privacy of network users

Natural Language Processing and Machine Learning as Practical Toolsets for Archival Processing

Peer ReviewedPurpose – This study aims to provide an overview of recent efforts relating to natural language processing (NLP) and machine learning applied to archival processing, particularly appraisal and sensitivity reviews, and propose functional requirements and workflow considerations for transitioning from experimental to operational use of these tools. Design/methodology/approach – The paper has four main sections. 1) A short overview of the NLP and machine learning concepts referenced in the paper. 2) A review of the literature reporting on NLP and machine learning applied to archival processes. 3) An overview and commentary on key existing and developing tools that use NLP or machine learning techniques for archives. 4) This review and analysis will inform a discussion of functional requirements and workflow considerations for NLP and machine learning tools for archival processing. Findings – Applications for processing e-mail have received the most attention so far, although most initiatives have been experimental or project based. It now seems feasible to branch out to develop more generalized tools for born-digital, unstructured records. Effective NLP and machine learning tools for archival processing should be usable, interoperable, flexible, iterative and configurable. Originality/value – Most implementations of NLP for archives have been experimental or project based. The main exception that has moved into production is ePADD, which includes robust NLP features through its named entity recognition module. This paper takes a broader view, assessing the prospects and possible directions for integrating NLP tools and techniques into archival workflows

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

The Use of Continuous Perioperative Dexmedetomidine Infusion to Reduce Opioid Consumption in Adult Patients Undergoing Spinal Lumbar Surgery: A Quality Improvement Project

Background: Exposure to opioids, preoperatively or during surgery, is a significant risk factor for developing opioid addiction and may increase the risk of acute tolerance and chronic use. More specifically, spinal lumbar surgery is associated with increased opioid requirements to counter associated pain secondary to lumbar manipulation. Currently, there is a lack of studies that exemplify the anesthetist’s role in minimizing narcotic use while effectively managing pain for this patient-specific population. Dexmedetomidine, an alpha-2 agonist, administered as a continuous infusion, has been shown to reduce opioid consumption in spinal lumbar surgery patients. Context: The implementation phase of this quality improvement project was completed through the voluntary participation of Miami Beach Anesthesiology Associates (MBAA) at Mount Sinai Medical Center. MBAA provides all anesthesia services for Mount Sinai Medical Center, a not for-profit, private teaching hospital located in Miami Beach, Florida. Objectives: The purpose of this study is to improve anesthesia provider knowledge on the role of continuous dexmedetomidine infusion to reduce opioid consumption in patients undergoing spinal lumbar surgery. A literature review including seven research studies addresses the PICO question “In adult patients undergoing lumbar spine surgery does the administration of continuous intravenous dexmedetomidine perioperatively compared to pain management with a traditional opioid approach lead to decreased perioperative opioid administration without an increase in reported pain postoperatively?” The literature review was used as the basis for this study and served as the educational framework to increase anesthesia provider knowledge. Methodology: The primary methodology used for the proposed project was administered through an online educational module. A pre-implementation survey assessed anesthesia provider knowledge of the current opioid crisis in the United States, dexmedetomidine’s role in reducing opioid requirements, and factors that have prevented the use of dexmedetomidine. Results: There was an overall improvement in anesthesia provider knowledge between pre-test and post-test survey responses following the online educational module. It can be assumed that most providers feel more inclined to use dexmedetomidine for this type of surgery. Conclusions: Currently, continuous dexmedetomidine infusion is not used as an adjuvant with opioids for spinal lumbar surgery patients. Fentanyl is the intraoperative opioid most utilized to control pain combined with other opioid and non-opioid drugs determined by individual providers. The educational intervention effectively improved provider awareness regarding opioid misuse risk factors, dexmedetomidine’s clinical uses, and favoring dexmedetomidine to reduce opioid consumption for this type of surgery. There are still factors that prevent the use of dexmedetomidine that may stem from the medical direction of other anesthesia providers that did not partake in this study