Aggregating Private and Public Web Archives Using the Mementity Framework

Web archives preserve the live Web for posterity, but the content on the Web one cares about may not be preserved. The ability to access this content in the future requires the assurance that those sites will continue to exist on the Web until the content is requested and that the content will remain accessible. It is ultimately the responsibility of the individual to preserve this content, but attempting to replay personally preserved pages segregates archived pages by individuals and organizations of personal, private, and public Web content. This is misrepresentative of the Web as it was. While the Memento Framework may be used for inter-archive aggregation, no dynamics exist for the special consideration needed for the contents of these personal and private captures. In this work we introduce a framework for aggregating private and public Web archives. We introduce three mementities that serve the roles of the aforementioned aggregation, access control to personal Web archives, and negotiation of Web archives in dimensions beyond time, inclusive of the dimension of privacy. These three mementities serve as the foundation of the Mementity Framework. We investigate the difficulties and dynamics of preserving, replaying, aggregating, propagating, and collaborating with live Web captures of personal and private content. We offer a systematic solution to these outstanding issues through the application of the framework. We ensure the framework\u27s applicability beyond the use cases we describe as well as the extensibility of reusing the mementities for currently unforeseen access patterns. We evaluate the framework by justifying the mementity design decisions, formulaically abstracting the anticipated temporal and spatial costs, and providing reference implementations, usage, and examples for the framework

Media aesthetics based multimedia storytelling.

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences --i.e., both image and video capture-- has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments --that describe the photographs, either in their local or on-line repositories-- are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images --e.g. images from online social networks to which the image stories are going to be uploaded--, and the collection images --i.e., the collection of images or videos that needs to be summarized into a story--, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models --both regression based, as well as classification based-- that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes.Postprint (published version

A Domain Specific Language for Digital Forensics and Incident Response Analysis

One of the longstanding conceptual problems in digital forensics is the dichotomy between the need for verifiable and reproducible forensic investigations, and the lack of practical mechanisms to accomplish them. With nearly four decades of professional digital forensic practice, investigator notes are still the primary source of reproducibility information, and much of it is tied to the functions of specific, often proprietary, tools. The lack of a formal means of specification for digital forensic operations results in three major problems. Specifically, there is a critical lack of: a) standardized and automated means to scientifically verify accuracy of digital forensic tools; b) methods to reliably reproduce forensic computations (their results); and c) framework for inter-operability among forensic tools. Additionally, there is no standardized means for communicating software requirements between users, researchers and developers, resulting in a mismatch in expectations. Combined with the exponential growth in data volume and complexity of applications and systems to be investigated, all of these concerns result in major case backlogs and inherently reduce the reliability of the digital forensic analyses. This work proposes a new approach to the specification of forensic computations, such that the above concerns can be addressed on a scientific basis with a new domain specific language (DSL) called nugget. DSLs are specialized languages that aim to address the concerns of particular domains by providing practical abstractions. Successful DSLs, such as SQL, can transform an application domain by providing a standardized way for users to communicate what they need without specifying how the computation should be performed. This is the first effort to build a DSL for (digital) forensic computations with the following research goals: 1) provide an intuitive formal specification language that covers core types of forensic computations and common data types; 2) provide a mechanism to extend the language that can incorporate arbitrary computations; 3) provide a prototype execution environment that allows the fully automatic execution of the computation; 4) provide a complete, formal, and auditable log of computations that can be used to reproduce an investigation; 5) demonstrate cloud-ready processing that can match the growth in data volumes and complexity

Building blocks for semantic data organization on the desktop

Die Organisation von (Multimedia-) Daten auf Desktop-Systemen wird derzeit hauptsächlich durch das Einordnen von Dateien in ein hierarchisches Dateisystem bewerkstelligt. Zusätzlich werden gewisse Inhalte (z.B. Musik oder Fotos) von spezialisierter Software mit Hilfe Datei-bezogener Metadaten verwaltet. Diese Metadaten werden meist direkt im Dateikopf in einer Unzahl verschiedener, vorwiegend proprietärer Formate gespeichert. Allgemein nehmen Metadaten und Links die Schlüsselrollen in fortgeschrittenen Datenorganisationskonzepten ein, ihre eingeschränkte Unterstützung in vorherrschenden Dateisystemen macht die Einführung solcher Konzepte auf dem Desktop jedoch schwierig: Erstens müssen Anwendungen sowohl Dateiformat als auch Metadatenschema verstehen um auf Metadaten zugreifen zu können; zweitens ist ein getrennter Zugriff auf Daten und Metadaten nicht möglich und drittens kann man solche Metadaten nicht mit mehreren Dateien oder mit Dateiordnern assoziieren obgleich letztere die derzeit wichtigsten Konstrukte für die Dateiorganisation darstellen. Dies bedeutet in weiterer Folge: (i) eingeschränkte Möglichkeiten der Datenorganisation, (ii) eingeschränkte Navigationsmöglichkeiten, (iii) schlechte Auffindbarkeit der gespeicherten Daten, und (iv) Fragmentierung von Metadaten. Obschon es Versuche gab, diese Situation (zum Beispiel mit Hilfe semantischer Dateisysteme) zu verbessern, wurden die meisten dieser Probleme bisher vor allem im Web und im Speziellen im semantischen Web adressiert und gelöst. Das Anwenden dort entwickelter Lösungen auf dem Desktop, einer zentralen Plattform der Daten- und Metadatenmanipulation, wäre zweifellos von Vorteil. In der vorliegenden Arbeit wird ein neues, rückwärts-kompatibles Metadatenmodell als Lösungsversuch für die oben genannten Probleme präsentiert. Dieses Modell basiert auf stabilen Datei-Identifikatoren und externen, semantischen, Datei- bezogenen Metadatenbeschreibungen welche im RDF Graphenmodell repräsentiert werden. Diese Beschreibungen sind durch eine einheitliche Linked-Data- Schnittstelle zugänglich und können mit anderen Beschreibungen und Ressourcen verlinkt werden. Im Speziellen erlaubt dieses Modell semantische Links zwischen lokalen Dateisystemobjekten und Netzressourcen im Web sowie im entstehenden “Daten Web” und ermöglicht somit die Integration dieser Datenräume. Das Modell hängt entscheidend von der Stabilität dieser Links ab weshalb zwei Algorithmen präsentiert werden, welche deren Integrität in lokalen und vernetzten Umgebungen erhalten können. Dies bedeutet, dass Links zwischen Dateisystemobjekten, Metadatenbeschreibungen und Netzressourcen nicht brechen wenn sich deren Adressen ändern, z.B. wenn Dateien verschoben oder Linked-Data Ressourcen unter geänderten URIs publiziert werden. Schließlich wird eine prototypische Implementierung des vorgeschlagenen Metadatenmodells präsentiert, welche demonstriert wie die Summe dieser Bausteine eine Metadatenschicht bildet die als Grundlage für semantische Datenorganisation auf dem Desktop verwendet werden kann.The organization of (multimedia) data on current desktop systems is done to a large part by arranging files in hierarchical file systems, but also by specialized applications (e.g., music or photo organizing software) that make use of file-related metadata for this task. These metadata are predominantly stored in embedded file headers, using a magnitude of mainly proprietary formats. Generally, metadata and links play the key roles in advanced data organization concepts. Their limited support in prevalent file system implementations, however, hinders the adoption of such concepts on the desktop: First, non-uniform access interfaces require metadata consuming applications to understand both a file’s format and its metadata scheme; second, separate data/metadata access is not possible, and third, metadata cannot be attached to multiple files or to file folders although the latter are the primary constructs for file organization. As a consequence of this, current desktops suffer, inter alia, from (i) limited data organization possibilities, (ii) limited navigability, (iii) limited data findability, and (iv) metadata fragmentation. Although there were attempts to improve this situation, e.g., by introducing semantic file systems, most of these issues were successfully addressed and solved in the Web and in particular in the Semantic Web and reusing these solutions on the desktop, a central hub of data and metadata manipulation, is clearly desirable. In this thesis a novel, backwards-compatible metadata model that addresses the above-mentioned issues is introduced. This model is based on stable file identifiers and external, file-related, semantic metadata descriptions that are represented using the generic RDF graph model. Descriptions are accessible via a uniform Linked Data interface and can be linked with other descriptions and resources. In particular, this model enables semantic linking between local file system objects and remote resources on the Web or the emerging Web of Data, thereby enabling the integration of these data spaces. As the model crucially relies on the stability of these links, we contribute two algorithms that preserve their integrity in local and in remote environments. This means that links between file system objects, metadata descriptions and remote resources do not break even if their addresses change, e.g., when files are moved or Linked Data resources are re-published using different URIs. Finally, we contribute a prototypical implementation of the proposed metadata model that demonstrates how these building blocks sum up to constitute a metadata layer that may act as a foundation for semantic data organization on the desktop

Privacy-Preserving Photo Taking and Accessing for Mobile Phones

Today, we are living in environments that are full of cameras embedded in devices such as smart phones and wearables. These mobile devices and as well as apps installed on them are designed to be extremely convenient for users to take, store and share photos. In spite of the convenience brought by ubiquitous cameras, users\u27 privacy may be breached through photos that are taken and stored with mobile devices. For example, when users take a photo of a scenery, a building or a target person, a stranger may also be unintentionally captured in the photo. Such photos expose the location and activity of strangers, and hence may breach their privacy. In addition, photos that are stored on smartphones may contain private information (e.g., driver\u27s license) about phone owners, which raise people’s privacy concerns over unauthorized access by installed apps. The goal of this dissertation is to protect people\u27s privacy in photo taking and accessing. To achieve this goal, we propose several systems to address the aforementioned privacy issues. To protect stranger\u27s privacy in photo taking, we proposed two systems called PrivacyCamera and PoliteCamera. Through cooperation between the photographer and the stranger, these systems can automatically blur the stranger’s face in the photo upon the stranger’s request when the photo is being taken. Even though PrivacyCamera and PoliteCamera can successfully protect stranger\u27s privacy, they depend on the cooperation between the photographer and the stranger. That requires both the photographer and stranger to install the proposed systems on their mobile phones; however, this is not always possible. Therefore, we further propose a feature-based model to automatically distinguish the target from strangers in a photo, so that we can blur all strangers\u27 faces without the cooperation. Finally, we designed PhotoSafer, a content-based and context-aware to protect private photos from unauthorized access on Android phones. In future work, we plan to design a privacy-preserving online sharing system, which has less burden of policy settings and can protect the privacy of strangers in a photo. In addition, we will also consider designing personalized systems to protect user-specific private photos

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience