pinecone/core-2020-05-10-deduplication huggingface dataset:
Browser Randomisation against Fingerprinting: A Quantitative Information Flow Approach

International audienceWeb tracking companies use device fingerprinting to distinguish the users of the websites by checking the numerous properties of their machines and web browsers. One way to protect the users' privacy is to make them switch between different machine and browser configurations. We propose a formalisation of this privacy enforcement mechanism. We use information-theoretic channels to model the knowledge of the tracker and the fingerprinting program, and show how to synthesise a randomisation mechanism that defines the distribution of configurations for each user. This mechanism provides a strong guarantee of privacy (the probability of identifying the user is bounded by a given threshold) while maximising usability (the user switches to other configurations rarely). To find an optimal solution, we express the enforcement problem of randomisation by a linear program. We investigate and compare several approaches to randomisation and find that more efficient privacy enforcement would often provide lower usability. Finally, we relax the requirement of knowing the fingerprinting program in advance, by proposing a randomisation mechanism that guarantees privacy for an arbitrary program
On the probability density function of baskets

The state price density of a basket, even under uncorrelated Black-Scholes
dynamics, does not allow for a closed from density. (This may be rephrased as
statement on the sum of lognormals and is especially annoying for such are used
most frequently in Financial and Actuarial Mathematics.) In this note we
discuss short time and small volatility expansions, respectively. The method
works for general multi-factor models with correlations and leads to the
analysis of a system of ordinary (Hamiltonian) differential equations.
Surprisingly perhaps, even in two asset Black-Scholes situation (with its flat
geometry), the expansion can degenerate at a critical (basket) strike level; a
phenomena which seems to have gone unnoticed in the literature to date.
Explicit computations relate this to a phase transition from a unique to more
than one "most-likely" paths (along which the diffusion, if suitably
conditioned, concentrates in the afore-mentioned regimes). This also provides a
(quantifiable) understanding of how precisely a presently out-of-money basket
option may still end up in-the-money.Comment: Appeared in: Large Deviations and Asymptotic Methods in Finance,
  Springer proceedings in Mathematics & Statistics, Editors: Friz, P.K.,
  Gatheral, J., Gulisashvili, A., Jacquier, A., Teichmann, J., 2015, with minor
  typos remove
The relevance of model-driven engineering thirty years from now

Although model-driven engineering (MDE) is now an established approach for developing complex software systems, it has not been universally adopted by the software industry. In order to better understand the reasons for this, as well as to identify future opportunities for MDE, we carried out a week-long design thinking experiment with 15 MDE experts. Participants were facilitated to identify the biggest problems with current MDE technologies, to identify grand challenges for society in the near future, and to identify ways that MDE could help to address these challenges. The outcome is a reflection of the current strengths of MDE, an outlook of the most pressing challenges for society at large over the next three decades, and an analysis of key future MDE research opportunities
A Native Versioning Concept to Support Historized Models at Runtime

International audienceModels@run.time provides semantically rich reflection lay-ers enabling intelligent systems to reason about themselves and their surrounding context. Most reasoning processes require not only to ex-plore the current state, but also the past history to take sustainable decisions e.g. to avoid oscillating between states. Models@run.time and model-driven engineering in general lack native mechanisms to efficiently support the notion of history, and current approaches usually generate redundant data when versioning models, which reasoners need to nav-igate. Because of this limitation, models fail in providing suitable and sustainable abstractions to deal with domains relying on history-aware reasoning. This paper tackles this issue by considering history as a na-tive concept for modeling foundations. Integrated, in conjunction with lazy load/storage techniques, into the Kevoree Modeling Framework, we demonstrate onto a smart grid case study, that this mechanisms enable a sustainable reasoning about massive historized models
Parsing in a Broad Sense

Having multiple representations of the same instance is common in software language engineering: models can be visualised as graphs, edited as text, serialised as XML. When mappings between such representations are considered, terms “parsing” and “unparsing” are often used with incompatible meanings and varying sets of underlying assumptions. We investigate 12 classes of artefacts found in software language processing, present a case study demonstrating their implementations and state-of-the-art mappings among them, and systematically explore the technical research space of bidirectional mappings to build on top of the existing body of work and discover as of yet unused relationships
Extreme State Aggregation beyond MDPs

We consider a Reinforcement Learning setup without any (esp. MDP) assumptions on the environment. State aggregation and more generally feature reinforcement learning is concerned with mapping histories/raw-states to reduced/aggregated states. The idea behind both is that the resulting reduced process (approximately) forms a small stationary finite-state MDP, which can then be efficiently solved or learnt. We considerably generalize existing aggregation results by showing that even if the reduced process is not an MDP, the (q-)value functions and (optimal) policies of an associated MDP with same state-space size solve the original problem, as long as the solution can approximately be represented as a function of the reduced states. This implies an upper bound on the required state space size that holds uniformly for all RL problems. It may also explain why RL algorithms designed for MDPs sometimes perform well beyond MDPs
Indefinitely Oscillating Martingales

We construct a class of nonnegative martingale processes
that oscillate indefinitely with high probability. For these processes, we
state a uniform rate of the number of oscillations for a given magnitude
and show that this rate is asymptotically close to the theoretical upper
bound. These bounds on probability and expectation of the number of
upcrossings are compared to classical bounds from the martingale literature.
We discuss two applications. First, our results imply that the
limit of the minimum description length operator may not exist. Second,
we give bounds on how often one can change one’s belief in a given
hypothesis when observing a stream of data
Preventing Premature Convergence and Proving the Optimality in Evolutionary Algorithms

Evolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality
Countering Ballot Stuffing and Incorporating Eligibility Verifiability in Helios

Helios is a web-based end-to-end verifiable electronic voting system which has been said to be suitable for low-coercion environments. Although many Internet voting schemes have been proposed in the literature, Helios stands out for its real world relevance. It has been used in a number of elections in university campuses around the world and it has also been used recently by the IACR to elect its board members. It was noted that a dishonest server in Helios can stuff ballots and this seems to limit the claims of end-to-end verifiability of the system. In this work, we investigate how the issue of ballot stuffing can be addressed with minimum change to the current vote casting experience in Helios and we argue formally about the security of our techniques. Our ideas are intuitive and general enough to be applied in the context of other Internet voting scheme and they also address recent attacks exploiting the malleability of ballots in Helios
Interaction history based answer formulation for question answering

With the rapid growth in information access methodologies, question answering has drawn considerable attention among others. Though question answering has emerged as an interesting new research domain, still it is vastly concentrated on question processing and answer extraction approaches. Latter steps like answer ranking, formulation and presentations are not treated in depth. Weakness we found in this arena is that answers that a particular user has acquired are not considered, when processing new questions. As a result, current systems are not capable of linking two questions such as “When is the Apple founded?” with a previously processed question “When is the Microsoft founded?” generating an answer in the form of “Apple is founded one year later Microsoft founded, in 1976”. In this paper we present an approach towards question answering to devise an answer based on the questions already processed by the system for a particular user which is termed as interaction history for the user. Our approach is a combination of question processing, relation extraction and knowledge representation with inference models. During the process we primarily focus on acquiring knowledge and building up a scalable user model to formulate future answers based on current answers that same user has processed. According to evaluation we carried out based on the TREC resources shows that proposed technology is promising and effective in question answering
GRL: A Specification Language for Globally Asynchronous Locally Synchronous Systems

International audienceA GALS (Globally Asynchronous, Locally Synchronous) system consists of several synchronous subsystems that evolve concurrently and interact with each other asynchronously. Most formalisms and design tools support either the synchronous paradigm or the asynchronous paradigm but rarely combine both, which requires an intricate modeling of GALS systems. In this paper, we present a new language, called GRL (GALS Representation Language) designed to model GALS systems in an abstract and versatile manner for the purpose of formal verification. GRL has formal semantics combining the synchronous reactive model underlying dataflow languages and the asynchronous concurrent model underlying process algebras. We present the basic concepts and the main constructs of the language, together with an illustrative example
Improving the Model Checking of Strategies under Partial Observability and Fairness Constraints

Reasoning about strategies has been a concern for several years, and many extensions of Alternating-time Temporal Logic have been proposed. One extension, ATLK_irF , allows the user to reason about the strategies of the agents of a system under partial observability and unconditional fairness constraints. However, the existing model-checking algorithm for ATLK_irF is inefficient when the user is only interested in the satisfaction of a formula in a small subset of states, such as the set of initial states of the system. We propose to generate fewer strategies by only focusing on partial strategies reachable from this subset of states, reducing the time needed to perform the verification. We also describe several practical improvements to further reduce the verification time and present experiments showing the practical impact of the approach
Insights into Entity Name Evolution on Wikipedia

Working with Web archives raises a number of issues caused by their temporal
characteristics. Depending on the age of the content, additional knowledge
might be needed to find and understand older texts. Especially facts about
entities are subject to change. Most severe in terms of information retrieval
are name changes. In order to find entities that have changed their name over
time, search engines need to be aware of this evolution. We tackle this problem
by analyzing Wikipedia in terms of entity evolutions mentioned in articles
regardless the structural elements. We gathered statistics and automatically
extracted minimum excerpts covering name changes by incorporating lists
dedicated to that subject. In future work, these excerpts are going to be used
to discover patterns and detect changes in other sources. In this work we
investigate whether or not Wikipedia is a suitable source for extracting the
required knowledge.Comment: WISE 2014, Thessaloniki, Greec
Incorporating the position of sharing action in predicting popular videos in online social networks

LNCS v.8787 entitled: Web Information Systems Engineering - WISE 2014: 15th International Conference, Thessaloniki, Greece, October 12-14, 2014, Proceedings, Part 2Predicting popular videos in online social networks (OSNs) is important for network traffic engineering and video recommendation. In order to avoid the difficulty of acquiring all OSN users’ activities, recent studies try to predict popular media contents in OSNs only based on a very small number of users, referred to as experts. However, these studies simply treat all users’ diffusion actions as the same. Based on large-scale video diffusion traces collected from a popular OSN, we analyze the positions of users’ video sharing actions in the propagation graph, and classify users’ video sharing actions into three different types, i.e., initiator actions, spreader actions and follower actions. Surprisingly, while existing studies mainly focus on the initiators, our empirical studies suggest that the spreaders actually play a more important role in the diffusion process of popular videos. Motivated by this finding, we account for the position information of sharing actions to select initiator experts, spreader experts and follower experts, based on corresponding sharing actions. We conduct experiments on the collected dataset to evaluate the performance of these three types of experts in predicting popular videos. The evaluation results demonstrate that the spreader experts can not only make more accurate predictions than initiator experts and follower experts, but also outperform the general experts selected by existing studies.postprin
BOSTER : an efficient algorithm for mining frequent unordered induced subtrees

Extracting frequent subtrees from the tree structured data has important applications in Web mining. In this paper, we introduce a novel canonical form for rooted labelled unordered trees called the balanced-optimal-search canonical form (BOCF) that can handle the isomorphism problem efficiently. Using BOCF, we define a tree structure guided scheme based enumeration approach that systematically enumerates only the valid subtrees. Finally, we present the balanced optimal search tree miner (BOSTER) algorithm based on BOCF and the proposed enumeration approach, for finding frequent induced subtrees from a database of labelled rooted unordered trees. Experiments on the real datasets compare the efficiency of BOSTER over the two state-of-the-art algorithms for mining induced unordered subtrees, HybridTreeMiner and UNI3. The results are encouraging
Two approaches to the dataset interlinking recommendation problem

Whenever a dataset t is published on the Web of Data, an exploratory search over existing datasets must be performed to identify those datasets that are potential candidates to be interlinked with t. This paper introduces and compares two approaches to address the dataset interlinking recommendation problem, respectively based on Bayesian classifiers and on Social Network Analysis techniques. Both approaches define rank score functions that explore the vocabularies, classes and properties that the datasets use, in addition to the known dataset links. After extensive experiments using real-world datasets, the results show that the rank score functions achieve a mean average precision of around 60%. Intuitively, this means that the exploratory search for datasets to be interlinked with t might be limited to just the top-ranked datasets, reducing the cost of the dataset interlinking process. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-11749-2_25.EC/FP7/LinkedUpCNPq/160326/2012-5CNPq/303332/2013-1CNPq/557128/2009-9FAPERJ/E-26/170028/2008FAPERJ/E-26/103.070/2011FAPERJ/E-26/101.382/2014CAPES/141082
A review selection method using product feature taxonomy

As of today, online reviews have become more and more important in decision making process. In recent years, the problem of identifying useful reviews for users has attracted significant attentions. For instance, in order to select reviews that focus on a particular feature, researchers proposed a method which extracts all associated words of this feature as the relevant information to evaluate and find appropriate reviews. However, the extraction of associated words is not that accurate due to the noise in free review text, and this affects the overall performance negatively. In this paper, we propose a method to select reviews according to a given feature by using a review model generated based upon a domain ontology called product feature taxonomy. The proposed review model provides relevant information about the hierarchical relationships of the features in the review which captures the review characteristics accurately. Our experiment results based on real world review dataset show that our approach is able to improve the review selection performance according to the given criteria effectively
Coherent Multi-Sentence Video Description with Variable Level of Detail

Humans can easily describe what they see in a coherent way and at varying
level of detail. However, existing approaches for automatic video description
are mainly focused on single sentence generation and produce descriptions at a
fixed level of detail. In this paper, we address both of these limitations: for
a variable level of detail we produce coherent multi-sentence descriptions of
complex videos. We follow a two-step approach where we first learn to predict a
semantic representation (SR) from video and then generate natural language
descriptions from the SR. To produce consistent multi-sentence descriptions, we
model across-sentence consistency at the level of the SR by enforcing a
consistent topic. We also contribute both to the visual recognition of objects
proposing a hand-centric approach as well as to the robust generation of
sentences using a word lattice. Human judges rate our multi-sentence
descriptions as more readable, correct, and relevant than related work. To
understand the difference between more detailed and shorter descriptions, we
collect and analyze a video description corpus of three levels of detail.Comment: 10 page
Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model
  with Salient Points

Hand motion capture has been an active research topic in recent years,
following the success of full-body pose tracking. Despite similarities, hand
tracking proves to be more challenging, characterized by a higher
dimensionality, severe occlusions and self-similarity between fingers. For this
reason, most approaches rely on strong assumptions, like hands in isolation or
expensive multi-camera systems, that limit the practical use. In this work, we
propose a framework for hand tracking that can capture the motion of two
interacting hands using only a single, inexpensive RGB-D camera. Our approach
combines a generative model with collision detection and discriminatively
learned salient points. We quantitatively evaluate our approach on 14 new
sequences with challenging interactions.Comment: German Conference on Pattern Recognition (GCPR) 2014,
  http://files.is.tue.mpg.de/dtzionas/GCPR_2014.htm
Nerve Detection in Ultrasound Images Using Median Gabor Binary Pattern

International audienceUltrasound in regional anesthesia (RA) has increased in pop-ularity over the last years. The nerve localization presents a key step for RA practice, it is therefore valuable to develop a tool able to facilitate this practice. The nerve detection in the ultrasound images is a challeng-ing task, since the noise and other artifacts corrupt the visual properties of such kind of tissue. In this paper we propose a new method to address this problem. The proposed technique operates in two steps. As the me-dian nerve belongs to a hyperechoic region, the first step consists in the segmentation of this type of region using the k-means algorithm. The second step is more critical; it deals with nerve structure detection in noisy data. For that purpose, a new descriptor is developed. It combines tow methods median binary pattern (MBP) and Gabor filter to obtain the median Gabor binary pattern (MGBP). The method was tested on 173 ultrasound images of the median nerve obtained from three patients. The results showed that the proposed approach achieves better accuracy than the original MBP, Gabor descriptor and other popular descriptors
Adaptive Feature Selection for Object Tracking with Particle Filter

International audienceObject tracking is an important topic in the field of computer vision. Commonly used color-based trackers are based on a fixed set of color features such as RGB or HSV and, as a result, fail to adapt to changing illumination conditions and background clutter. These drawbacks can be overcome to an extent by using an adaptive framework which selects for each frame of a sequence the features that best discriminate the object from the background. In this paper, we use such an adaptive feature selection method embedded into a particle filter mechanism and show that our tracking method is robust to lighting changes and background distractions. Different experiments also show that the proposed method outperform other approaches
Biologically Inspired Vision for Indoor Robot Navigation

Ultrasonic, infrared, laser and other sensors are being applied
in robotics. Although combinations of these have allowed robots to navigate,
they are only suited for specific scenarios, depending on their limitations.
Recent advances in computer vision are turning cameras into useful
low-cost sensors that can operate in most types of environments. Cameras
enable robots to detect obstacles, recognize objects, obtain visual
odometry, detect and recognize people and gestures, among other possibilities.
In this paper we present a completely biologically inspired vision
system for robot navigation. It comprises stereo vision for obstacle detection,
and object recognition for landmark-based navigation. We employ
a novel keypoint descriptor which codes responses of cortical complex
cells. We also present a biologically inspired saliency component, based
on disparity and colour