pinecone/core-2020-05-10-deduplication huggingface dataset:
Evolving Spatially Aggregated Features from Satellite Imagery for
  Regional Modeling

Satellite imagery and remote sensing provide explanatory variables at
relatively high resolutions for modeling geospatial phenomena, yet regional
summaries are often desirable for analysis and actionable insight. In this
paper, we propose a novel method of inducing spatial aggregations as a
component of the machine learning process, yielding regional model features
whose construction is driven by model prediction performance rather than prior
assumptions. Our results demonstrate that Genetic Programming is particularly
well suited to this type of feature construction because it can automatically
synthesize appropriate aggregations, as well as better incorporate them into
predictive models compared to other regression methods we tested. In our
experiments we consider a specific problem instance and real-world dataset
relevant to predicting snow properties in high-mountain Asia
RK-EDA: a novel random key based estimation of distribution algorithm.

The challenges of solving problems naturally represented as permutations by Estimation of Distribution Algorithms (EDAs) have been a recent focus of interest in the evolutionary computation community. One of the most common alternative representations for permutation based problems is the Random Key (RK), which enables the use of continuous approaches for this problem domain. However, the use of RK in EDAs have not produced competitive results to date and more recent research on permutation based EDAs have focused on creating superior algorithms with specially adapted representations. In this paper, we present RK-EDA; a novel RK based EDA that uses a cooling scheme to balance the exploration and exploitation of a search space by controlling the variance in its probabilistic model. Unlike the general performance of RK based EDAs, RK-EDA is actually competitive with the best EDAs on common permutation test problems: Flow Shop Scheduling, Linear Ordering, Quadratic Assignment, and Travelling Salesman Problems
Feature-based diversity optimization for problem instance classification

Parallel Problem Solving from Nature – PPSN XIVUnderstanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson problem. In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances that are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.Wanru Gao, Samadhi Nallaperuma, and Frank Neuman
Convergence versus diversity in multiobjective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkConvergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting based selection method. While convergence-first sorting has continuously shown effectiveness for handling a variety of problems, it faces challenges to maintain well population diversity due to the overemphasis of convergence. In this paper, we propose a general diversity-first sorting method for multiobjective optimization. Based on the method, a new MOEA, called DBEA, is then introduced. DBEA is compared with the recently-developed nondominated sorting genetic algorithm III (NSGA-III) on different problems. Experimental studies show that the diversity-first method has great potential for diversity maintenance and is very competitive for many-objective optimization
The RAGE Software Asset Model and Metadata Model

Software assets are key output of the RAGE project and they can be used by applied game developers to enhance the pedagogical and educational value of their games. These software assets cover a broad spectrum of functionalities – from player analytics including emotion detection to intelligent adaptation and social gamiﬁcation. In order to facilitate integration and interoperability, all of these assets adhere to a common model, which describes their properties through a set of metadata. In this paper the RAGE asset model and asset metadata model is presented, capturing the detail of assets and their potential usage within three distinct dimensions – technological, gaming and pedagogical. The paper highlights key issues and challenges in constructing the RAGE asset and asset metadata model and details the process and design of a ﬂexible metadata editor that facilitates both adaptation and improvement of the asset metadata model.This study is part of the RAGE project. The RAGE project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187.  This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains
Choose your own uni adventure: An orientation and transition game

University orientation and transition strategies enjoy limited success,in part due to poor attendance, but largely due to the inability of these programs to communicate expectations in a meaningful way. The solution offered here is an educational game, not as an alternative to orientation, but as a complementary, self-directed orientation and transition experience. \ud
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Choose your own Uni Adventure teaches the language and practices of university and allow players to draw inferences about university values and expectations in a way that will engender engagement and motivation. The aim of the project is to create a proof of concept for the game, the design of which is described here
Collaborative business process modeling in multi-surface environments

Analyzing and redesigning business processes is a complex task which requires the collaboration of multiple actors such as process stakeholders, domain experts and others. Current collaborative modeling approaches mainly focus on modeling workshops where participants verbally contribute their perspective on a process along with ideas on how to improve it. These workshops are supported by modeling experts who facilitate the workshop and translate participants’ verbal contributions into a process model. Being limited to verbal contributions however might negatively affect the motivation of participants to actively contribute. Interactive technology such as smartphones, tablets, digital tabletops and interactive walls can provide opportunities for participants to directly interact with process models. Multi surface environments where different interactive technologies (e.g. display walls, tabletops, tablets, and mobiles) are combined also allow for orchestrating different modes of collaboration. In this chapter we describe an approach that combines different styles of collaboration using various interactive surfaces in a multi surface environment. Testing this approach in three different settings we found indications that interactive technology not only improves involvement by participants but also speeds up workshops and improves the quality of collaboration outcomes. The studies also revealed means for improving the proposed approach
A Belief-Based Approach to Measuring Message Acceptability

International audienceWe propose a formal framework to support belief revision based on a cognitive model of credibility and trust. In this framework, the acceptance of information coming from a source depends on (i) the agent's goals and beliefs about the source's goals, (ii) the credibility, for the agent, of incoming information, and (iii) the agent's beliefs about the context in which it operates. This makes it possible to approach belief revision in a setting where new incoming information is associated with an acceptance degree. In particular, such degree may be used as input weight for any possibilistic conditioning operator with uncertain input (i.e., weighted belief revision operator)
Preference inference based on Pareto models

In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model. We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model
Reasoning with Multiple-Agent Possibilistic Logic

In multiple-agent logic, a formula is in the form of (a, A) where a is a propositional formula and A is a subset of agents. It states that at least all agents in A believe that a is true. This paper presents a method of refutation for this logic, based on a general resolution principle and using a linear strategy, which is sound and complete. This strategy is then extended so as to deal with certainty levels. It manipulates formulas in the form (a,α/A) expressing that all agents in set A believe at least at some level α that a is true. Finally, an experimental study is provided with the aim to estimate the performance of the proposed algorithms
Graphical Models for Preference Representation: An Overview

Representing preferences into a compact structure has become an important research topic. Graphical models are of special interest. Indeed, they facilitate elicitation, exhibit some form of independence, and serve as a basis for solving optimization and dominance queries about choices. The expressiveness of the representation setting and the complexity of answering queries are then central issues for each approach. This paper proposes an extensive overview of the main graphical models for preference representation and provides a comparative survey by emphasizing their main characteristics. We also indicate possible transformations between some of these models. We contrast qualitative models such as CP-nets and TCP-nets with quantitative ones such as GAI networks, UCP-nets, and Marginal utility nets, and advocate π-Pref nets, recently introduced by the authors, as an interesting compromise between the two types of models
Fuzzy Labeling for Abstract Argumentation: An Empirical Evaluation

International audienceArgumentation frameworks have to be evaluated with respect to argumentation semantics to compute the set(s) of accepted arguments. In a previous approach, we proposed a fuzzy labeling algorithm for computing the (fuzzy) set of acceptable arguments, when the sources of the arguments in the argumentation framework are only partially trusted. The convergence of the algorithm was proved, and the convergence speed was estimated to be linear, as it is generally the case with iterative methods. In this paper, we provide an experimental validation of this algorithm with the aim of carrying out an empirical evaluation of its performance on a benchmark of argumentation graphs. Results show the satisfactory performance of our algorithm, even on complex graph structures as those present in our benchmark
Universally Composable Two-Server PAKE

Two-Server Password Authenticated Key Exchange (2PAKE) protocols apply secret shar-ing techniques to achieve protection against server-compromise attacks. 2PAKE protocols eliminate the need for password hashing and remain secure as long as one of the servers remains honest. This concept has also been explored in connection with two-server password authenticated secret sharing (2PASS) protocols for which game-based and universally composable versions have been proposed. In contrast, universally composable PAKE protocols exist currently only in the single-server scenario and all proposed 2PAKE protocols use game-based security deﬁnitions. In this paper we propose the ﬁrst construction of an universally composable 2PAKE protocol, alongside with its ideal functionality. The protocol is proven UC-secure in the standard model, assuming a common reference string which is a common assumption to many UC-secure PAKE and PASS protocols. The proposed protocol remains secure for arbitrary password distributions. As one of the building blocks we deﬁne and construct a new cryptographic primitive, called Trapdoor Distributed Smooth Projective Hash Function (TD-SPHF), which could be of independent interest
Blind Password Registration for Two-Server Password Authenticated Key Exchange and Secret Sharing Protocols

Many organisations enforce policies on the length and formation of passwords to encourage selection of strong passwords and protect their multi-user systems. For Two-Server Password Authenticated Key Exchange (2PAKE) and Two-Server Password Authenticated Secret Sharing (2PASS) protocols, where the password chosen by the client is secretly shared between the two servers, the initial remote registration of policy-compliant passwords represents a major problem because none of the servers is supposed to know the password in clear. We solve this problem by introducing Two-Server Blind Password Registration (2BPR) protocols that can be executed between a client and the two servers as part of the remote registration procedure. 2BPR protocols guarantee that secret shares sent to the servers belong to a password that matches their combined password policy and that the plain password remains hidden from any attacker that is in control of at most one server. We propose a security model for 2BPR protocols capturing the requirements of policy compliance for client passwords and their blindness against the servers. Our model extends the adversarial setting of 2PAKE/2PASS protocols to the registration phase and hence closes the gap in the formal treatment of such protocols. We construct an efficient 2BPR protocol for ASCII-based password policies, prove its security in the standard model, give a proof of concept implementation, and discuss its performance
Ethnicity sensitive author disambiguation using semi-supervised learning

Author name disambiguation in bibliographic databases is the problem of grouping together scientific publications written by the same person, accounting for potential homonyms and/or synonyms. Among solutions to this problem, digital libraries are increasingly offering tools for authors to manually curate their publications and claim those that are theirs. Indirectly, these tools allow for the inexpensive collection of large annotated training data, which can be further leveraged to build a complementary automated disambiguation system capable of inferring patterns for identifying publications written by the same person. Building on more than 1 million publicly released crowdsourced annotations, we propose an automated author disambiguation solution exploiting this data (i) to learn an accurate classifier for identifying coreferring authors and (ii) to guide the clustering of scientific publications by distinct authors in a semi-supervised way. To the best of our knowledge, our analysis is the first to be carried out on data of this size and coverage. With respect to the state of the art, we validate the general pipeline used in most existing solutions, and improve by: (i) proposing phonetic-based blocking strategies, thereby increasing recall; and (ii) adding strong ethnicity-sensitive features for learning a linkage function, thereby tailoring disambiguation to non-Western author names whenever necessary.Peer reviewe
From Traditional to Modern : Domain Adaptation for Action Classification
  in Short Social Video Clips

Short internet video clips like vines present a significantly wild
distribution compared to traditional video datasets. In this paper, we focus on
the problem of unsupervised action classification in wild vines using
traditional labeled datasets. To this end, we use a data augmentation based
simple domain adaptation strategy. We utilise semantic word2vec space as a
common subspace to embed video features from both, labeled source domain and
unlablled target domain. Our method incrementally augments the labeled source
with target samples and iteratively modifies the embedding function to bring
the source and target distributions together. Additionally, we utilise a
multi-modal representation that incorporates noisy semantic information
available in form of hash-tags. We show the effectiveness of this simple
adaptation technique on a test set of vines and achieve notable improvements in
performance.Comment: 9 pages, GCPR, 201
Artistic style transfer for videos

In the past, manually re-drawing an image in a certain artistic style
required a professional artist and a long time. Doing this for a video sequence
single-handed was beyond imagination. Nowadays computers provide new
possibilities. We present an approach that transfers the style from one image
(for example, a painting) to a whole video sequence. We make use of recent
advances in style transfer in still images and propose new initializations and
loss functions applicable to videos. This allows us to generate consistent and
stable stylized video sequences, even in cases with large motion and strong
occlusion. We show that the proposed method clearly outperforms simpler
baselines both qualitatively and quantitatively.Comment: final version appeared in GCPR-2016; minor changes to improve the
  clarit
Depth Estimation Through a Generative Model of Light Field Synthesis

Light field photography captures rich structural information that may
facilitate a number of traditional image processing and computer vision tasks.
A crucial ingredient in such endeavors is accurate depth recovery. We present a
novel framework that allows the recovery of a high quality continuous depth map
from light field data. To this end we propose a generative model of a light
field that is fully parametrized by its corresponding depth map. The model
allows for the integration of powerful regularization techniques such as a
non-local means prior, facilitating accurate depth map estimation.Comment: German Conference on Pattern Recognition (GCPR) 201
Testing the robustness of laws of polysemy and brevity versus frequency

The pioneering research of G.K. Zipf on the relationship between word frequency and other word features led to the formulation of various linguistic laws. Here we focus on a couple of them: the meaning-frequency law, i.e. the tendency of more frequent words to be more polysemous, and the law of abbreviation, i.e. the tendency of more frequent words to be shorter. Here we evaluate the robustness of these laws in contexts where they have not been explored yet to our knowledge. The recovery of the laws again in new conditions provides support for the hypothesis that they originate from abstract mechanisms.Peer ReviewedPostprint (author's final draft
Novel lightweight signcryption-based key distribution mechanisms for MIKEY

Part 1: Authentication and Key ManagementInternational audienceMultimedia Internet KEYing (MIKEY) is a standard key management protocol, used to set up common secrets between any two parties for multiple scenarios of communications. As MIKEY becomes widely deployed, it becomes worthwhile to not confine its applications to real-time or other specific applications, but also to extend the standard to other scenarios as well. For instance, MIKEY can be used to secure key establishment in the Internet of Things. In this particular context, Elliptic Curve Cryptography-based (ECC) algorithms seem to be good candidate to be employed by MIKEY, since they can support equivalent security level when compared with other recommended cryptographic algorithms like RSA, and at the same time requiring smaller key sizes and offering better performance. In this work, we propose novel lightweight ECC-based key distribution extensions for MIKEY that are built upon a previously proposed certificateless signcryption scheme. To our knowledge, these extensions are the first ECC-based MIKEY extensions that employ signcryption schemes. Our proposed extensions benefit from the lightness of the signcryption scheme, while being discharged from the burden of the public key infrastructure (PKI) thanks to its certificateless feature. To demonstrate their performance, we implemented our proposed extensions in the Openmote sensor platform and conducted a thorough performance assessment by measuring the energy consumption and execution time of each operation in the key establishment procedure. The experimental results prove that our new MIKEY extensions are perfectly suited for resource-constrained device
Security Challenges of Small Cell as a Service in Virtualized Mobile Edge Computing Environments

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the rst to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the rst paper that proposes a set of criteria to facilitate a clear and e ective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial e ect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users
Formal Verification of a Rover Anti-collision System

In this paper, we integrate inductive proof, bounded model checking, test case generation and equivalence proof techniques to verify an embedded system. This approach is implemented using Systerel Smart Solver (S3) toolset. It is applied to verify properties at system, software, and code levels. The verification process is illustrated on an anti-collision system (ARP for Automatic Rover Protection) implemented on-board a rover. Focus is placed on the verification of safety and functional properties and the proof of equivalence between the design model and the generated code