Marketplace of Ideas, Privacy, and the Digital Audience

The availability of almost limitless sets of digital information has opened a vast marketplace of ideas. Information service providers like Facebook and Twitter provide users with an array of personal information about products, friends, acquaintances, and strangers. While this data enriches the lives of those who share content on the internet, it comes at the expense of privacy. Social media companies disseminate news, advertisements, and political messages, while also capitalizing on consumers’ private shopping, surfing, and traveling habits. Companies like Cambridge Analytica, Amazon, and Apple rely on algorithmic programs to mash up and scrape enormous amounts of online and otherwise available personal data to microtarget audiences. By collecting and then processing psychometric data sets, commercial and political advertisers rely on emotive advertisements to manipulate biases and vulnerabilities that impact audiences’ shopping and voting habits. The Free Speech Clause is not an absolute bar to the regulation of commercial intermediaries who exploit private information obtained on the digital marketplace of ideas. The Commerce Clause authorizes passage of laws to regulate internet companies that monetize intimate data and resell it to third parties. Rather than applying strict scrutiny to such proposed regulations as one would to pure speech, judges should rely on intermediate scrutiny to test statutes limiting the commercial marketing of data. Legislative reforms are needed to address the substantial economic effects of massive, commercial agglomeration of data files containing histories, daily routines, medical conditions, personal habits, and the like. To address this logarithmically expanding cyberphenomenon, Congress should temporally restrict the retention and trade in private data. Internet intermediaries should not be immune from such a restriction on private data storage. For such a policy to be effective, safe harbor provisions shielding internet intermediaries should be modified to allow for civil litigation against internet companies that refuse a data subject’s request to remove personal information no longer needed to accomplish the transaction for which it was originally processed

Web analytics in usability evaluation of websites

Verkkosivun käytettävyydellä on merkittävä vaikutus siihen, miten verkkosivu onnistuu sille asetetuissa tavoitteissa. Käytettävyyttä tutkitaan perinteisesti menetelmillä, jotka yritykset kokevat kalliiksi ja hitaiksi nopeasti muuttuvassa toimintaympäristössä. Verkkosivujen tutkimiseen on kuitenkin mahdollista käyttää myös web-analytiikkaa, jonka tarjoamilla menetelmillä ja työkaluilla voidaan kerätä tietoa verkkosivun käyttäjistä. Kerätyn tiedon avulla käytettävyyttä voidaan arvioida tutkimalla verkkosivun käyttäjien vuorovaikutusta verkkosivulla, mikä mahdollistaa käytettävyyden arvioimisen pienemmillä kustannuksilla ja lyhyemmässä ajassa. Tutkimuksen päätavoite oli tarjota kokonaiskuva web-analytiikasta sekä sen rajoitteista verkkosivujen käytettävyyden arvioinnissa. Tutkimus suoritettiin systemaattisena kirjallisuuskatsauksena. Tutkimusaineisto valittiin tieteellisistä hakuportaaleista etsitystä materiaalista. Tutkimusaineiston perusteella esitettiin web-analytiikan käyttötapoja ja tavoitteita sekä käytön rajoitteita ja haasteita verkkosivujen käytettävyyden arvioinnissa. Web-analytiikkaa käytetään verkkosivujen käytettävyyden arvioinnissa käytettävyysongelmien tunnistamiseen, käytettävyyden tason arvioimiseen sen osatekijöitä mittaamalla, käyttäjien vuorovaikutuksen yleispiirteiden tunnistamiseen, verkkosivun visuaalisen ilmeen vaihtoehtojen vertailuun sekä automaattisena tiedon keräämisen välineenä muun käytettävyyden tutkimuksen osana. Tutkimuksen tulosten perusteella voidaan lisäksi todeta, ettei web-analytiikka sovellu osaksi tuotekehityksen nopeita iteraatioita eikä se sovellu käytettäväksi ainoana käytettävyyden arvioinnin menetelmänä, kun tarvitaan syvä ymmärrys käyttäjien vuorovaikutuksesta verkkosivulla. Web-analytiikka soveltuu kuitenkin hyvin muiden arvioinnin menetelmien tueksi ja tarjoaa kustannustehokkaan työkalun tiedon keräämiseen verkkosivun käyttäjistä. Web-analytiikalla voidaan optimoida muun käytettävyyden arvioinnin menetelmiä ja sitä kautta vähentää käytettävyyden arvioinnin kustannuksia

Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Mapping the Current Landscape of Research Library Engagement with Emerging Technologies in Research and Learning: Final Report

The generation, dissemination, and analysis of digital information is a significant driver, and consequence, of technological change. As data and information stewards in physical and virtual space, research libraries are thoroughly entangled in the challenges presented by the Fourth Industrial Revolution:1 a societal shift powered not by steam or electricity, but by data, and characterized by a fusion of the physical and digital worlds.2 Organizing, structuring, preserving, and providing access to growing volumes of the digital data generated and required by research and industry will become a critically important function. As partners with the community of researchers and scholars, research libraries are also recognizing and adapting to the consequences of technological change in the practices of scholarship and scholarly communication. Technologies that have emerged or become ubiquitous within the last decade have accelerated information production and have catalyzed profound changes in the ways scholars, students, and the general public create and engage with information. The production of an unprecedented volume and diversity of digital artifacts, the proliferation of machine learning (ML) technologies,3 and the emergence of data as the “world’s most valuable resource,”4 among other trends, present compelling opportunities for research libraries to contribute in new and significant ways to the research and learning enterprise. Librarians are all too familiar with predictions of the research library’s demise in an era when researchers have so much information at their fingertips. A growing body of evidence provides a resounding counterpoint: that the skills, experience, and values of librarians, and the persistence of libraries as an institution, will become more important than ever as researchers contend with the data deluge and the ephemerality and fragility of much digital content. This report identifies strategic opportunities for research libraries to adopt and engage with emerging technologies,5 with a roughly fiveyear time horizon. It considers the ways in which research library values and professional expertise inform and shape this engagement, the ways library and library worker roles will be reconceptualized, and the implication of a range of technologies on how the library fulfills its mission. The report builds on a literature review covering the last five years of published scholarship, primarily North American information science literature, and interviews with a dozen library field experts, completed in fall 2019. It begins with a discussion of four cross-cutting opportunities that permeate many or all aspects of research library services. Next, specific opportunities are identified in each of five core research library service areas: facilitating information discovery, stewarding the scholarly and cultural record, advancing digital scholarship, furthering student learning and success, and creating learning and collaboration spaces. Each section identifies key technologies shaping user behaviors and library services, and highlights exemplary initiatives. Underlying much of the discussion in this report is the idea that “digital transformation is increasingly about change management”6 —that adoption of or engagement with emerging technologies must be part of a broader strategy for organizational change, for “moving emerging work from the periphery to the core,”7 and a broader shift in conceptualizing the research library and its services. Above all, libraries are benefitting from the ways in which emerging technologies offer opportunities to center users and move from a centralized and often siloed service model to embedded, collaborative engagement with the research and learning enterprise

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Education alignment

This essay reviews recent developments in embedding data management and curation skills into information technology, library and information science, and research-based postgraduate courses in various national contexts. The essay also investigates means of joining up formal education with professional development training opportunities more coherently. The potential for using professional internships as a means of improving communication and understanding between disciplines is also explored. A key aim of this essay is to identify what level of complementarity is needed across various disciplines to most effectively and efficiently support the entire data curation lifecycle

Cultural Heritage on line

The 2nd International Conference "Cultural Heritage online – Empowering users: an active role for user communities" was held in Florence on 15-16 December 2009. It was organised by the Fondazione Rinascimento Digitale, the Italian Ministry for Cultural Heritage and Activities and the Library of Congress, through the National Digital Information Infrastructure and Preservation Program - NDIIP partners. The conference topics were related to digital libraries, digital preservation and the changing paradigms, focussing on user needs and expectations, analysing how to involve users and the cultural heritage community in creating and sharing digital resources. The sessions investigated also new organisational issues and roles, and cultural and economic limits from an international perspective

Resource discovery in heterogeneous digital content environments

The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice

The text classification pipeline: Starting shallow, going deeper

An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC.An increasingly relevant and crucial subfield of Natural Language Processing (NLP), tackled in this PhD thesis from a computer science and engineering perspective, is the Text Classification (TC). Also in this field, the exceptional success of deep learning has sparked a boom over the past ten years. Text retrieval and categorization, information extraction and summarization all rely heavily on TC. The literature has presented numerous datasets, models, and evaluation criteria. Even if languages as Arabic, Chinese, Hindi and others are employed in several works, from a computer science perspective the most used and referred language in the literature concerning TC is English. This is also the language mainly referenced in the rest of this PhD thesis. Even if numerous machine learning techniques have shown outstanding results, the classifier effectiveness depends on the capability to comprehend intricate relations and non-linear correlations in texts. In order to achieve this level of understanding, it is necessary to pay attention not only to the architecture of a model but also to other stages of the TC pipeline. In an NLP framework, a range of text representation techniques and model designs have emerged, including the large language models. These models are capable of turning massive amounts of text into useful vector representations that effectively capture semantically significant information. The fact that this field has been investigated by numerous communities, including data mining, linguistics, and information retrieval, is an aspect of crucial interest. These communities frequently have some overlap, but are mostly separate and do their research on their own. Bringing researchers from other groups together to improve the multidisciplinary comprehension of this field is one of the objectives of this dissertation. Additionally, this dissertation makes an effort to examine text mining from both a traditional and modern perspective. This thesis covers the whole TC pipeline in detail. However, the main contribution is to investigate the impact of every element in the TC pipeline to evaluate the impact on the final performance of a TC model. It is discussed the TC pipeline, including the traditional and the most recent deep learning-based models. This pipeline consists of State-Of-The-Art (SOTA) datasets used in the literature as benchmark, text preprocessing, text representation, machine learning models for TC, evaluation metrics and current SOTA results. In each chapter of this dissertation, I go over each of these steps, covering both the technical advancements and my most significant and recent findings while performing experiments and introducing novel models. The advantages and disadvantages of various options are also listed, along with a thorough comparison of the various approaches. At the end of each chapter, there are my contributions with experimental evaluations and discussions on the results that I have obtained during my three years PhD course. The experiments and the analysis related to each chapter (i.e., each element of the TC pipeline) are the main contributions that I provide, extending the basic knowledge of a regular survey on the matter of TC