pinecone/core-2020-05-10-deduplication huggingface dataset:
Fast ALS-based tensor factorization for context-aware recommendation
  from implicit feedback

Albeit, the implicit feedback based recommendation problem - when only the
user history is available but there are no ratings - is the most typical
setting in real-world applications, it is much less researched than the
explicit feedback case. State-of-the-art algorithms that are efficient on the
explicit case cannot be straightforwardly transformed to the implicit case if
scalability should be maintained. There are few if any implicit feedback
benchmark datasets, therefore new ideas are usually experimented on explicit
benchmarks. In this paper, we propose a generic context-aware implicit feedback
recommender algorithm, coined iTALS. iTALS apply a fast, ALS-based tensor
factorization learning method that scales linearly with the number of non-zero
elements in the tensor. The method also allows us to incorporate diverse
context information into the model while maintaining its computational
efficiency. In particular, we present two such context-aware implementation
variants of iTALS. The first incorporates seasonality and enables to
distinguish user behavior in different time intervals. The other views the user
history as sequential information and has the ability to recognize usage
pattern typical to certain group of items, e.g. to automatically tell apart
product types or categories that are typically purchased repetitively
(collectibles, grocery goods) or once (household appliances). Experiments
performed on three implicit datasets (two proprietary ones and an implicit
variant of the Netflix dataset) show that by integrating context-aware
information with our factorization framework into the state-of-the-art implicit
recommender algorithm the recommendation quality improves significantly.Comment: Accepted for ECML/PKDD 2012, presented on 25th September 2012,
  Bristol, U
The current state of research on eGovernment in developing countries: A literature review

Published version of a chapter in the book: Electronic Government. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-33489-4_1This paper reports a review of literature on eGovernment in the context of developing countries published between 2005 and 2010. The insights emerging from this review may guide researchers in their continued investigation of eGovernment implementation, especially in the context of developing countries. From a review of 108 papers, the study found some substantive changes in the field of eGovernment research. These included increased adoption of interpretive paradigm and increased use of theories in the research. Some future research direction from the methodological perspective were provided: paying more attention to research paradigm and methodology, preserving multiculturalism in eGovernment research, encouraging action research and longitudinal studies, and improving the research quality by grounding it on theories
E-government in Tanzania: Current status and future challenges

Article from the book: Electronic Government. Also available from SpringerLink: http://dx.doi.org/10.1007/978-3-642-33489-4_17The public sector plays an important role in the economic growth and development of developing countries. The application of modern Information and Communication Technologies (ICT) may help improve the public sector by contributing to new services and processes that address citizens as well as government-to-government services, involve citizens more directly in decisions being made, and contribute to streamlining work processes and standardizations needed to develop a well-functioning public sector. Research focusing on E-government in developing countries is still dominated by case studies and conceptual pieces of work. Thus, more empirical-oriented work is needed to expand our knowledge on current status, challenges and future plans. The reported study has been initiated to address such needs. The objective is to investigate, from a broad perspective, on-going E-government initiatives in the public sector in Tanzania. The contribution of this work is twofold. First, the descriptive findings are important to gain insight into the current status of E-government projects in Tanzania. Second, the study reported here could guide the way forward for practice as well as research. We firmly believe that both practice and research should be based on the current situation and identified challenges and aim to describe such issues to guide future work
Assessing effects of eGovernment initiatives based on a public value framework

Published version of a chapter in the book: Electronic Government. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-33489-4_21Assessing effects of eGovernment initiatives is considered an important but challenging endeavor. Assessments are, among other things, important to justify e-government investments. They are challenging because they are complex, often based on locally defined indicators, many times over-emphasizing financial effects, imprecise, faced with a number of contingencies and very seldom validated. Consequently, effect assessments can be seen as imprecise and difficult to compare across different initiatives. This paper addresses some of the challenges by attempting to assess effects based on a public value framework through an action design study with a Norwegian government agency. Based on our findings, we suggest 5 design principles for adapting and using performance indicators for assessing effects from eGovernment initiatives
Towards finding relational redescriptions

acceptance rate 47.8%This paper introduces relational redescription mining, that is,
the task of finding two structurally different patterns
that describe nearly the same set of object tuples in a relational
dataset. 
By extending 
  redescription mining beyond propositional and real-valued
  attributes, it provides a powerful tool to match different relational
  descriptions of the same concept. 
As a first step towards solving this general task, we introduce an efficient algorithm that  
mines one description of a given binary concept.  A set of graph patterns 
 is built from
frequent path patterns connecting example pairs. Experiments in the domain of explaining
kinship terms show that this approach can produce complex descriptions
that match explanations by domain experts, while being much faster than a direct relational query mining
approach.status: publishe
A method for measuring the creative potential of computer games

This paper describes a method for measuring the creative potential of computer games. The research approach applies a behavioral and verbal protocol to analyze the factors that influence the creative processes used by people as they play computer games from the puzzle genre. Creative potential is measured by examining task motivation and domain-relevant and creativity-relevant skills. This paper focuses on the reliability of the factors used for measurement, determining those factors that are more strongly related to creativity. The findings show that creative potential may be determined by examining the relationship between skills required and the effect of intrinsic motivation within game play activities
Mobile Application for Noise Pollution Monitoring through Gamification Techniques

Full data coverage of urban environments is crucial to monitor the status of the area to detect, for instance, trends and detrimental environmental changes. Collecting observations related to environmental factors such as noise pollution in urban environments through classical approaches implies the deployment of Sensor Networks. The cost of deployment and maintenance of such infrastructure might be relatively high for local and regional governments. On the other hand recent mass-market mobile devices such as smartphones are full of sensors. For instance, it is possible to perform measurements of noise through its microphone. Therefore they become low-cost measuring devices that many citizens have in their pocket. In this paper we present an approach for gathering noise pollution data by using mobile applications. The applications are designed following gamification techniques to encourage users to participate using their personal smartphones. In this way the users are involved in taking and sharing noise pollution measurements in their cities that other stakeholders can use in their analysis and decision making processes
Local vs global descriptors of hippocampus shape evolution for Alzheimer's longitudinal population analysis

International audienceIn the context of Alzheimer's disease (AD), state-of-the-art methods separating normal control (NC) from AD patients or CN from progressive MCI (mild cognitive impairment patients converting to AD) achieve decent classification rates. However, they all perform poorly at separating stable MCI (MCI patients not converting to AD) and progressive MCI. Instead of using features extracted from a single temporal point, we address this problem using descriptors of the hippocampus evolutions between two time points. To encode the transformation, we use the framework of large deformations by diffeomorphisms that provides geodesic evolutions. To perform statistics on those local features in a common coordinate system, we introduce an extension of the K\"{a}rcher mean algorithm that defines the template modulo rigid registrations, and an initialization criterion that provides a final template leading to better matching with the patients. Finally, as local descriptors transported to this template do not directly perform as well as global descriptors (e.g. volume difference), we propose a novel strategy combining the use of initial momentum from geodesic shooting, extended K\"{a}rcher algorithm, density transport and integration on a hippocampus subregion, which is able to outperform global descriptors
Boosting local consistency algorithms over oating-point numbers

International audienceSolving constraints over oating-point numbers is a critical issue in numerous applications notably in program verication. Capa-bilities of ltering algorithms over the oating-point numbers (F) have been so far limited to 2b-consistency and its derivatives. Though safe, such ltering techniques suer from the well known pathological prob-lems of local consistencies, e.g., inability to eciently handle multiple occurrences of the variables. These limitations also have their origins in the strongly restricted oating-point arithmetic. To circumvent the poor properties of oating-point arithmetic, we propose in this paper a new ltering algorithm, called FPLP, which relies on various relaxations over the real numbers of the problem over F. Safe bounds of the domains are computed with a mixed integer linear programming solver (MILP) on safe linearizations of these relaxations. Preliminary experiments on a relevant set of benchmarks are promising and show that this approach can be eective for boosting local consistency algorithms over F
Refining Abstract Interpretation Based Value Analysis with Constraint Programming Techniques

International audienceAbstract interpretation based value analysis is a classical approach for verifying programs with floating-point computations. However, state-of-the-art tools compute an over-approximation of the variable values that can be very coarse. In this paper, we show that constraint solvers can significantly refine the approximations computed with abstract interpretation tools. We introduce a hybrid approach that combines abstract interpretation and constraint programming techniques in a single static and automatic analysis. RAICP, the system we developed is substantially more precise than FLUCTUAT, a state-of-the-art static analyser. Moreover, it could eliminate 13 false alarms generated by FLUCTUAT on a standard set of benchmarks
Cardinality Reasoning for Bin-Packing Constraint: Application to a Tank Allocation Problem

International audienceFlow reasoning has been successfully used in CP for more than a decade. It was originally introduced by Régin in the well-known Alldifferent and Global Cardinality Constraint (GCC) available in most of the CP solvers. The BinPacking constraint was introduced by Shaw and mainly uses an independent knapsack reasoning in each bin to filter the possible bins for each item. This paper considers the use of a cardinal-ity/flow reasoning for improving the filtering of a bin-packing constraint. The idea is to use a GCC as a redundant constraint to the BinPacking that will count the number of items placed in each bin. The cardinality variables of the GCC are then dynamically updated during the propagation. The cardinality reasoning of the redundant GCC makes deductions that the bin-packing constraint cannot see since the placement of all items into every bin is considered at once rather than for each bin individually. This is particularly well suited when a minimum loading in each bin is specified in advance. We apply this idea on a Tank Allocation Problem (TAP). We detail our CP model and give experimental results on a real-life instance demonstrating the added value of the cardinality reasoning for the bin-packing constraint. This constraint enforces the relation L j = i (X i = j) · w i , ∀j. It makes the link between n weighted items (item i has a weight w i) and the m different capacitated bins in which they are to be put. Only the weights of the items are integers, the other arguments of the constraints are finite domain (f.d.) variables. Note that in this formulation, Lj is a variable which is bounded by the maximal capacity of the bin j. Without loss of generality we assume the item variables and their weights are sorted such that w i ≤ w i+1. Example: BinP acking([1, 4, 1, 2, 2], [2, 3, 3, 3, 4], [5, 7, 0, 3])
Surface segmentation for improved remeshing

Many remeshing techniques sample the input surface in a meaningful way and then triangulate the samples to produce an output triangulated mesh. One class of methods samples in a parametrization of the surface. Another class samples directly on the surface. These latter methods must have sufficient density of samples to achieve outputs that are homeomorphic to the input. In many datasets samples must be very dense even in some nearly planar regions due to small local feature size. We present an isotropic remeshing algorithm called κCVT that achieves topological correctness while sampling sparsely in all flat regions, regardless of local feature size. This is accomplished by segmenting the surface, remeshing the segmented subsurfaces individually and then stitching them back together. We show that κCVT produces quality meshes using fewer triangles than other methods. The output quality meshes are both homeomorphic and geometrically close to the input surface.postprin
Prone to Supine CT Colonography Registration Using a Landmark and Intensity Composite Method

Matching corresponding location between prone and supine acquisitions for CT colonography (CTC) is essential to verify the existence of a polyp, which can be a difficult task due to the considerable deformations that will often occur to the colon during repositioning of the patient. This can induce error and increase interpretation time. We propose a novel method to automatically establish correspondence between the two acquisitions. A first step segments a set of haustral folds in each view and determines correspondence via a labelling process using a Markov Random Field (MRF) model. We show how the landmark correspondences can be used to non-rigidly transform a 2D source image derived from a conformal mapping process on the 3D endoluminal surface mesh to achieve full surface correspondence between prone and supine views. This can be used to initialise an intensity-based non-rigid B-spline registration method which further increases the accuracy. We demonstrate a statistically significant improvement over the intensity based non-rigid B-spline registration by using the composite method
A non-intrusive movie recommendation system

Several recommendation systems have been developed to support the user in choosing an interesting movie from multimedia repositories. The widely utilized collaborative-filtering systems focus on the analysis of user profiles or user ratings of the items. However, these systems decrease their performance at the start-up phase and due to privacy issues, when a user hides most of his personal data. On the other hand, content-based recommendation systems compare movie features to suggest similar multimedia contents; these systems are based on less invasive observations, however they find some difficulties to supply tailored suggestions. In this paper, we propose a plot-based recommendation system, which is based upon an evaluation of similarity among the plot of a video that was watched by the user and a large amount of plots that is stored in a movie database. Since it is independent from the number of user ratings, it is able to propose famous and beloved movies as well as old or unheard movies/programs that are still strongly related to the content of the video the user has watched. We experimented different methodologies to compare natural language descriptions of movies (plots) and evaluated the Latent Semantic Analysis (LSA) to be the superior one in supporting the selection of similar plots. In order to increase the efficiency of LSA, different models have been experimented and in the end, a recommendation system that is able to compare about two hundred thousands movie plots in less than a minute has been developed
DEDISbench: a benchmark for deduplicated storage systems

Lecture Notes in Computer Science, 7566Deduplication is widely accepted as an effective technique for eliminating duplicated data in backup and archival systems. Nowadays, deduplication is also becoming appealing in cloud computing, where large-scale virtualized storage infrastructures hold huge data volumes with a significant share of duplicated content. There have thus been several proposals for embedding deduplication in storage appliances and file systems, providing different performance trade-offs while targeting both user and application data, as well as virtual machine images.
It is however hard to determine to what extent is deduplication useful in a particular setting and what technique will provide the best results. In fact, existing disk I/O micro-benchmarks are not designed for evaluating deduplication systems, following simplistic approaches for generating data written that lead to unrealistic amounts of duplicates.
We address this with DEDISbench, a novel micro-benchmark for evaluating disk I/O performance of block based deduplication systems. As the main contribution, we introduce the generation of a realistic duplicate distribution based on real datasets. Moreover, DEDISbench also allows simulating access hotspots and different load intensities for I/O operations. The usefulness of DEDISbench is shown by comparing it with Bonnie++ and IOzone open-source disk I/O micro-benchmarks on assessing two open-source deduplication systems, Opendedup and Lessfs, using Ext4 as a baseline. As a secondary contribution, our results lead to novel insight on the performance of these file systems
An ontology-based model for SME network contracts

Even if collaboration is considered an effective solution to improve business strategies, SMEs often lack common principles and common forms of contractual coordination. Several policies implemented by E.U. have addressed the setup of a comprehensive SME policy framework. However, European institutions seem to have focused more on organizational devices to conduct business activities rather than on contractual forms of coordination. In April 2009, Italy adopted a law in network contract to promote the development of interfirm cooperation strategies to foster enterprises' innovation and growth. Even if this law represents a novelty in Europe and may offer new challenges and hints, it still presents some lacks in its formulation. The current research aims at presenting the Italian law for network contract, by highlighting both its potentialities and its defects. A formal model to support the design of a SME network was proposed, by providing both an ontology-based model to help the definition of the contract in a structured way, and a basic workflow to identify the important phases of the network design, i.e., the feasibility study and the negotiation. In this way, the network rules and criteria for controlling the network members' contributions are defined. Mathematical tools derived from performance optimization were exploite
Towards a Trust and Reputation Framework for Social Web Platforms

best full paper + posterInternational audienceTrust and Reputation Systems (TRSs) represent a significant trend in decision support for Internet-based interactions. They help users to decide whom to trust and how much to trust a transaction. They are also an effective mechanism to encourage honesty and cooperation among users, resulting in healthy online markets or communities. The basic idea is to let parties rate each other so that new public knowledge can be created from personal experiences. The major difficulty in designing a reputation system is making it robust against malicious attacks. Our contribution in this paper is twofold. Firstly, we combine multiple research agendas into a holistic approach to building a robust TRS. Secondly, we focus on one TRS component which is the reputation computing engine and provide a novel investigation into an implementation of the engine proposed in [7]
An Architecture for Data and Knowledge Acquisition for the Semantic Web: the AGROVOC Use Case

We are surrounded by ever growing volumes of unstructured and weakly-structured information, and for a human being, domain expert or not, it is nearly impossible to read, understand and categorize such information in a fair amount of time. Moreover, different user categories have different expectations: final users need easy-to-use tools and services for specific tasks, knowledge engineers require robust tools for knowledge acquisition, knowledge categorization and semantic resources development, while semantic applications developers demand for flexible frameworks for fast and easy, standardized development of complex applications. This work represents an experience report on the use of the CODA framework for rapid prototyping and deployment of knowledge acquisition systems for RDF. The system integrates independent NLP tools and custom libraries complying with UIMA standards. For our experiment a document set has been processed to populate the AGROVOC thesaurus with two new relationships
Interactive public digital displays: investigating its use in a high school context

This paper presents a longitudinal user study that investigated the
adoption of some Bluetooth based functionalities for a public digital display
in a high school. More specifically, the utilization of Bluetooth device naming
extended beyond social identity representation and introduced the use of a
simple interaction mechanism. The interaction mechanism involves recognizing
parts of the Bluetooth device name as explicit instructions to trigger the
generation of content on an interactive public display. Together with representatives
of the teachers' community, the design team defined some social
rules concerning usage in order to account for the specificities of the place. In
the user study, three fully functional prototypes were deployed at the school
hall of the high school. The functionalities introduced with the different prototypes
were: the visualization on the display of the Bluetooth device names,
the possibility to contribute to tag clouds and the possibility to choose icons
from a given set for self-expression. The results suggest that people appropriated
some but not all of the functionalities employed. Implications of our
findings to the design of interactive digital displays are pointed out