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Data standardization
With data rapidly becoming the lifeblood of the global economy, the ability to improve its use significantly affects both social and private welfare. Data standardization is key to facilitating and improving the use of data when data portability and interoperability are needed. Absent data standardization, a âTower of Babelâ of different databases may be created, limiting synergetic knowledge production. Based on interviews with data scientists, this Article identifies three main technological obstacles to data portability and interoperability: metadata uncertainties, data transfer obstacles, and missing data. It then explains how data standardization can remove at least some of these obstacles and lead to smoother data flows and better machine learning. The Article then identifies and analyzes additional effects of data standardization. As shown, data standardization has the potential to support a competitive and distributed data collection ecosystem and lead to easier policing in cases where rights are infringed or unjustified harms are created by data-fed algorithms. At the same time, increasing the scale and scope of data analysis can create negative externalities in the form of better profiling, increased harms to privacy, and cybersecurity harms. Standardization also has implications for investment and innovation, especially if lock-in to an inefficient standard occurs. The Article then explores whether market-led standardization initiatives can be relied upon to increase welfare, and the role governmental-facilitated data standardization should play, if at all
An Architectural Solution of Assistance e-Services for Diabetes Diet
The aim of this paper is to outline the requirements and main architecture for a useful tool for determining the nutrition facts of food for people having Type 2 Diabetes. This diabetes is used only to establish the target audience, a ĂąâŹĆmass of peopleù⏠having, maybe, to less in common regarding the computer usage skills. The characteristics of the target audience (huge number, diversity of habits and behaviors, computer usage skills) requires a solution based on web services delivered at least partly as a standalone/ portable application, build from Web services and provided with means for domain knowledge dissemination and usage.Software Architecture, Knowledge Management, SIK, Business Rules, Type 2 Diabetes
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Organization and Usage of Learning Objects within Personal Computers
Research report of the ProLearn Network of Excellence (IST 507310), Deliverable 7.6To promote the integration of Desktop related Knowledge Management and Technology Enhanced Learning this deliverable aims at increasing the awareness of Desktop research within the Professional Learning community and at familiarizing the e-Learning researchers with the state-of-the-art in the relevant areas of Personal Information Management (PIM), as well as with the currently on-going activities and some of the regular PIM publication venues
A new technique for intelligent web personal recommendation
Personal recommendation systems nowadays are very important in web applications
because of the available huge volume of information on the World Wide Web, and the
necessity to save usersâ time, and provide appropriate desired information, knowledge,
items, etc. The most popular recommendation systems are collaborative filtering systems,
which suffer from certain problems such as cold-start, privacy, user identification, and
scalability. In this thesis, we suggest a new method to solve the cold start problem taking
into consideration the privacy issue. The method is shown to perform very well in
comparison with alternative methods, while having better properties regarding user privacy.
The cold start problem covers the situation when recommendation systems have not
sufficient information about a new userâs preferences (the user cold start problem), as well
as the case of newly added items to the system (the item cold start problem), in which case
the system will not be able to provide recommendations. Some systems use usersâ
demographical data as a basis for generating recommendations in such cases (e.g. the
Triadic Aspect method), but this solves only the user cold start problem and enforces userâs
privacy. Some systems use usersâ âstereotypesâ to generate recommendations, but
stereotypes often do not reflect the actual preferences of individual users. While some other
systems use userâs âfilterbotsâ by injecting pseudo users or bots into the system and consider
these as existing ones, but this leads to poor accuracy.
We propose the active node method, that uses previous and recent usersâ browsing targets
and browsing patterns to infer preferences and generate recommendations (node
recommendations, in which a single suggestion is given, and batch recommendations, in
which a set of possible target nodes are shown to the user at once). We compare the active
node method with three alternative methods (Triadic Aspect Method, NaĂŻve Filterbots
Method, and MediaScout Stereotype Method), and we used a dataset collected from online
web news to generate recommendations based on our method and based on the three
alternative methods. We calculated the levels of novelty, coverage, and precision in these
experiments, and we found that our method achieves higher levels of novelty in batch
recommendation while achieving higher levels of coverage and precision in node
recommendations comparing to these alternative methods. Further, we develop a variant of
the active node method that incorporates semantic structure elements. A further
experimental evaluation with real data and users showed that semantic node
recommendation with the active node method achieved higher levels of novelty than nonsemantic
node recommendation, and semantic-batch recommendation achieved higher levels
of coverage and precision than non-semantic batch recommendation
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
Social networks are considered to be heterogeneous graph neural networks
(HGNNs) with deep learning technological advances. HGNNs, compared to
homogeneous data, absorb various aspects of information about individuals in
the training stage. That means more information has been covered in the
learning result, especially sensitive information. However, the
privacy-preserving methods on homogeneous graphs only preserve the same type of
node attributes or relationships, which cannot effectively work on
heterogeneous graphs due to the complexity. To address this issue, we propose a
novel heterogeneous graph neural network privacy-preserving method based on a
differential privacy mechanism named HeteDP, which provides a double guarantee
on graph features and topology. In particular, we first define a new attack
scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we
design a two-stage pipeline framework, which includes the privacy-preserving
feature encoder and the heterogeneous link reconstructor with gradients
perturbation based on differential privacy to tolerate data diversity and
against the attack. To better control the noise and promote model performance,
we utilize a bi-level optimization pattern to allocate a suitable privacy
budget for the above two modules. Our experiments on four public benchmarks
show that the HeteDP method is equipped to resist heterogeneous graph privacy
leakage with admirable model generalization
Enabling Technologies for Web 3.0: A Comprehensive Survey
Web 3.0 represents the next stage of Internet evolution, aiming to empower
users with increased autonomy, efficiency, quality, security, and privacy. This
evolution can potentially democratize content access by utilizing the latest
developments in enabling technologies. In this paper, we conduct an in-depth
survey of enabling technologies in the context of Web 3.0, such as blockchain,
semantic web, 3D interactive web, Metaverse, Virtual reality/Augmented reality,
Internet of Things technology, and their roles in shaping Web 3.0. We commence
by providing a comprehensive background of Web 3.0, including its concept,
basic architecture, potential applications, and industry adoption.
Subsequently, we examine recent breakthroughs in IoT, 5G, and blockchain
technologies that are pivotal to Web 3.0 development. Following that, other
enabling technologies, including AI, semantic web, and 3D interactive web, are
discussed. Utilizing these technologies can effectively address the critical
challenges in realizing Web 3.0, such as ensuring decentralized identity,
platform interoperability, data transparency, reducing latency, and enhancing
the system's scalability. Finally, we highlight significant challenges
associated with Web 3.0 implementation, emphasizing potential solutions and
providing insights into future research directions in this field
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