pinecone/core-2020-05-10-deduplication huggingface dataset:
To encode or to propagate? The best choice for each constraint in SAT

Sophisticated compact SAT encodings exist for many types of constraints. Alternatively, for instances with many (or large) constraints, the SAT solver can also be extended with built-in propagators (the SAT Modulo Theories approach, SMT). For example, given a cardinality constraint x 1 + … + x n  ≤ k, as soon as k variables become true, such a propagator can set the remaining variables to false, generating a so-called explanation clause of the form x1∧…∧xk→xi¯ . But certain “bottle-neck” constraints end up generating an exponential number of explanations, equivalent to a naive SAT encoding, much worse than using a compact encoding with auxiliary variables from the beginning. Therefore, Abío and Stuckey proposed starting off with a full SMT approach and partially encoding, on the fly, only certain “active” parts of constraints. Here we build upon their work. Equipping our solvers with some additional bookkeeping to monitor constraint activity has allowed us to shed light on the effectiveness of SMT: many constraints generate very few, or few different, explanations. We also give strong experimental evidence showing that it is typically unnecessary to consider partial encodings: it is competitive to encode the few really active constraints entirely. This makes the approach amenable to any kind of constraint, not just the ones for which partial encodings are known.Postprint (author’s final draft
Global Inverse Consistency for Interactive Constraint Satisfaction

Some applications require the interactive resolution of a constraint problem by a human user. In such cases, it is highly desirable that the person who interactively solves the problem is not given the choice to select values that do not lead to solutions. We call this property global inverse consistency. Existing systems simulate this either by maintaining arc consistency after each assignment performed by the user or by compiling offline the problem as a multi-valued decision diagram. In this paper, we define several questions related to global inverse consistency and analyse their complexity. Despite their theoretical intractability, we propose several algorithms for enforcing global inverse consistency and we show that the best version is efficient enough to be used in an interactive setting on several configuration and design problems. We finally extend our contribution to the inverse consistency of tuples
Maintaining Soft Arc Consistency in BnB-ADOPT+ During Search

Gutierrez and Meseguer show how to enforce consistency in BnB-ADOPT + for distributed constraint optimization, but they consider unconditional deletions only. However, during search, more values can be pruned conditionally according to variable instantiations that define subproblems. Enforcing consistency in these subproblems can cause further search space reduction. We introduce efficient methods to maintain soft arc consistencies in every subproblem during search, a non trivial task due to asynchronicity and induced overheads. Experimental results show substantial benefits on three different benchmarks. © 2013 Springer-Verlag.The work of Gutierrez and Meseguer was partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya 2009-SGR-1434.Peer Reviewe
Multi-objective constraint optimization with tradeoffs

In this paper, we consider the extension of multi-objective constraint optimization algorithms to the case where there are additional tradeoffs, reducing the number of optimal solutions. We focus especially on branch-and-bound algorithms which use a mini-buckets algorithm for generating the upper bound at each node (in the context of maximizing values of objectives). Since the main bottleneck of these algorithms is the very large size of the guiding upper bound sets we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We also propose much faster dominance checks with respect to the preference relation induced by the tradeoffs. Furthermore, we show that our tradeoffs approach which is based on a preference inference technique can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before
Embarrassingly Parallel Search

International audienceWe propose the Embarrassingly Parallel Search, a simple and efficient method for solving constraint programming problems in parallel. We split the initial problem into a huge number of independent subproblems and solve them with available workers (i.e., cores of machines). The decomposition into subproblems is computed by selecting a subset of variables and by enumerating the combinations of values of these variables that are not detected inconsistent by the propagation mechanism of a CP Solver. The experiments on satisfaction problems and on optimization problems suggest that generating between thirty and one hundred subproblems per worker leads to a good scalability. We show that our method is quite competitive with the work stealing approach and able to solve some classical problems at the maximum capacity of the multi-core machines. Thanks to it, a user can parallelize the resolution of its problem without modifying the solver or writing any parallel source code and can easily replay the resolution of a problem
A parametric approach for smaller and better encodings of cardinality constraints

Adequate encodings for high-level constraints are a key ingredient for the application of SAT technology. In particular, cardinality constraints state that at most (at least, or exactly) k out of n propositional variables can be true. They are crucial in many applications. Although sophisticated encodings for cardinality constraints exist, it is well known that for small n and k straightforward encodings without auxiliary variables sometimes behave better, and that the choice of the right trade-off between minimizing either the number of variables or the number of clauses is highly application-dependent. Here we build upon previous work on Cardinality Networks to get the best of several worlds: we develop an arc-consistent encoding that, by recursively decomposing the constraint into smaller ones, allows one to decide which encoding to apply to each sub-constraint. This process minimizes a function λ·num- vars + num-clauses, where λ is a parameter that can be tuned by the user. Our careful experimental evaluation shows that (e.g., for λ = 5) this new technique produces much smaller encodings in variables and clauses, and indeed strongly improves SAT solvers' performance.Postprint (author’s final draft
Experiments on neuroevolution and online weight adaptation in complex environments

Neuroevolution has come a long way over the last decade. Lots of interesting and successful new methods and algorithms have been presented, with great improvements that make the field become very promising. Concretely, HyperNEAT has shown a great potential for evolving large scale neural networks, by discovering geometric regularities, thus being suitable for evolving complex controllers. However, once training phase has finished, evolved neural networks stay fixed and learning/adaptation does not happen anymore. A few methods have been proposed to address this concern, mainly using Hebbian plasticity and/or Compositional Pattern Producing Networks (CPPNs) like in Adaptive HyperNEAT. This methods have been tested in simple environments to isolate the effectiveness of adaptation from the Neuroevolution. In spite of this being quite convenient, more research is needed to better understand online adaptation in more complex environments. This paper shows a new proposal for online weight adaptation in neuroevolved artificial neural networks, and presents the results of several experiments carried out in a race simulation environment
Time-aware evaluation of methods for identifying active household members in recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_3Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.Online services are usually accessed via household accounts. A household account is typically shared by various users who live in the same house. This represents a problem for providing personalized services, such as recommendation. Identifying the household members who are interacting with an online system (e.g. an on-demand video service) in a given moment, is thus an interesting challenge for the recommender systems research community. Previous work has shown that methods based on the analysis of temporal patterns of users are highly accurate in the above task when they use randomly sampled test data. However, such evaluation methodology may not properly deal with the evolution of the users’ preferences and behavior through time. In this paper we evaluate several methods’ performance using time-aware evaluation methodologies. Results from our experiments show that the discrimination power of different time features varies considerably, and moreover, the accuracy achieved by the methods can be heavily penalized when using a more realistic evaluation methodology
A contextual modeling approach for model-based recommender systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-40643-0_5Proceedings of 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.In this paper we present a contextual modeling approach for model-based recommender systems that integrates and exploits both user preferences and contextual signals in a common vector space. Differently to previous work, we conduct a user study acquiring and analyzing a variety of realistic contextual signals associated to user preferences in several domains. Moreover, we report empirical results evaluating our approach in the movie and music domains, which show that enhancing model-based recommender systems with time, location and social companion information improves the accuracy of generated recommendations
An SNMP filesystem in userspace

Modern computer networks are constantly increasing in size and complexity. Despite this, data networks are a critical factor for the success of many organizations. Monitoring their health and operation sta- tus is fundamental, and usually performed through specific network man- agement architectures, developed and standardized in the last decades. On the other hand, file systems have become one of the best well known paradigms of human-computer interaction, and have been around since early days in the personal computer industry. In this paper we propose a file system interface to network management information, allowing users to open, edit and visualize network and systems operation information
On sets of numbers rationally represented in a rational base number
  system

In this work, it is proved that a set of numbers closed under addition and
whose representations in a rational base numeration system is a rational
language is not a finitely generated additive monoid.
  A key to the proof is the definition of a strong combinatorial property on
languages : the bounded left iteration property. It is both an unnatural
property in usual formal language theory (as it contradicts any kind of pumping
lemma) and an ideal fit to the languages defined through rational base number
systems
A generalization of bounds for cyclic codes, including the HT and BS
  bounds

We use the algebraic structure of cyclic codes and some properties of the
discrete Fourier transform to give a reformulation of several classical bounds
for the distance of cyclic codes, by extending techniques of linear algebra. We
propose a bound, whose computational complexity is polynomial bounded, which is
a generalization of the Hartmann-Tzeng bound and the Betti-Sala bound. In the
majority of computed cases, our bound is the tightest among all known
polynomial-time bounds, including the Roos bound
Studying stress on e-Learning users

Serie : Lecture notes in computer science, ISSN 0302-9743, vol. 8154E-Learning, much like any other communication processes, has been significantly shaped by technological evolution. In its original form, e-Learning aimed to bring the education closer to people, making it more modular and personalized. However, in reality, we observe that it represents a separation between student and teacher, simplifying this relationship to the exchange of ”text-based messages”, leaving aside all the important contextual richness of the classroom. We are addressing this issue by devising a contextual layer for e-Learning platforms. Particularly, in this paper we describe a solution to convey information about the level of stress of the students so that the teacher can take better and more informed decisions concerning the management of the learning process
On the predictability of stock market behavior using StockTwits sentiment and posting volume

Series title : Lecture notes in computer science, vol. 8154Inthisstudy,weexploreddatafromStockTwits,amicroblog- ging platform exclusively dedicated to the stock market. We produced several indicators and analyzed their value when predicting three market variables: returns, volatility and trading volume. For six major stocks, we measured posting volume and sentiment indicators. We advance on the previous studies on this subject by considering a large time period, using a robust forecasting exercise and performing a statistical test of forecasting ability. In contrast with previous studies, we find no evidence of return predictability using sentiment indicators, and of information content of posting volume for forecasting volatility. However, there is ev- idence that posting volume can improve the forecasts of trading volume, which is useful for measuring stock liquidity (e.g. assets easily sold).This work is funded by FEDER, through the program COMPETE and the Portuguese Foundation for Science and Technology (FCT), within the project FCOMP-01-0124-FEDER-022674. The also authors wish to thank StockTwits for kindly providing their data
SMOTE for regression

Several real world prediction problems involve forecasting rare values of a target variable. When this variable is nominal we have a problem of class imbalance that was already studied thoroughly within machine learning. For regression tasks, where the target variable is continuous, few works exist addressing this type of problem. Still, important application areas involve forecasting rare extreme values of a continuous target variable. This paper describes a contribution to this type of tasks. Namely, we propose to address such tasks by sampling approaches. These approaches change the distribution of the given training data set to decrease the problem of imbalance between the rare target cases and the most frequent ones. We present a modification of the well-known Smote algorithm that allows its use on these regression tasks. In an extensive set of experiments we provide empirical evidence for the superiority of our proposals for these particular regression tasks. The proposed SmoteR method can be used with any existing regression algorithm turning it into a general tool for addressing problems of forecasting rare extreme values of a continuous target variable
Dynamics of Relative Agreement in Multiple Social Contexts

In real world scenarios, the formation of consensus is an self-organisation process by which actors have to make a joint assessment about a target subject being it a decision making problem or the formation of a collective opinion. In social simulation, models of opinion dynamics tackle the opinion formation phenomena. These models try to make an assessment, for instance, of the ideal conditions that lead an interacting group of agents to opinion consensus, polarisation or fragmentation. In this paper, we investigate the role of social relation structure in opinion dynamics using an interaction model of relative agreement. We present an agent-based model that defines social relations as multiple concomitant social networks and apply our model to an opinion dynamics model with bounded confidence. We discuss the influence of complex social network topologies where actors interact in multiple relations simultaneously. The paper builds on previous work about social space design with multiple contexts and context switching, to determine the influence of such complex social structures in a process such as opinion formation
A Context Description Language for Medical Information Systems

Contextualized delivery of information is one of the many strengths of ubiquitous computing. It makes information actionable and helps us to better understand our situations. In the realm of healthcare, contextual information provides a terse but precise picture of the patient’s health situation. The patient context can have many facets, ranging from nutrition context over health heritage context to the context of symptoms, just to name a few. Setting up the correct health condition context of a patient favors better and faster recognition of the patient’s actual health situation. Context-awareness in medical monitoring mainly concentrates on gathering numerical facts depicting special aspects of a person’s health condition. In this paper we want to broaden the focus on the textual dimension in context development, by considering semantic annotation in designing context-awareness. We describe an approach for a context description language (CDL) that supports the uniform presentation of textual facts in medical reports and automatic reasoning on these facts. Term clusters in medical reports represent in a unique way symptoms and findings that set up the health context reflected in this particular report. These clusters manifest potential health condition contexts where a patient can be viewed in. A reasoning engine operates on these context presentations and selects those that match best the patient’s health situation. Locating the right context supports the physician in faster getting a first picture of the probable health situation of a new patient to be examined. We present experiments with a CDL applied on reports related to respiratory problems
10 years of probabilistic querying - what next?

invited paperOver the past decade, the two research areas of probabilistic databases and probabilistic programming have intensively studied the problem of making structured probabilistic inference scalable, but — so far — both areas developed almost independently of one another. While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineage, probabilistic programming has contributed sophisticated inference techniques based on knowledge compilation and lifted (first-order) inference. Both fields have developed their own variants of — both exact and approximate — top-k algorithms for query evaluation, and both investigate query optimization techniques known from SQL, Datalog, and Prolog, which all calls for a more intensive study of the commonalities and integration of the two fields. Moreover, we believe that natural-language processing and information extraction will remain a driving factor and in fact a longstanding challenge for developing expressive representation models which can be combined with structured probabilistic inference — also for the next decades to come.status: publishe
OLAP in Multifunction Multidimensional Database

Most models proposed for modeling multidimensional data warehouses consider a same function to determine how measure values are aggregated. We provide a more flexible conceptual model allowing associating each measure with several aggregation functions according to dimensions, hierarchies, and levels of granularity. This article studies the impacts of this model on the multidimensional table (MT) and the OLAP algebra [11]. It shows how the MT can handle several aggregation functions. It also introduces the changes of the internal mechanism of OLAP operators to take into account several aggregation functions especially if these functions are non-commutative
Resource Allocation for Query Optimization in Data Grid Systems: Static Load Balancing Strategies

Resource allocation is one of the principal stages of relational query processing in data grid systems. Static allocation methods allocate nodes to relational operations during query compilation. Existing heuristics did not take into account the multi-queries environment, where some nodes may become overloaded because they are allocated to too many concurrent queries. Dynamic resource allocation mechanisms are currently developed to modify the physical plan during query execution. In fact, when a node is detected to be overloaded, some of the operations on it will migrate. However, if the resource contention is too heavy in the initial execution plan, the operation migration cost may be very high. In this paper, we propose two load balancing strategies adopted during the static resource allocation phase, so that the workload is balanced at the beginning, the operation migration cost is decreased during the query execution, and therefore the average response time is reduced
Periodic Data, Burden or Convenience

Periodic events seem to be an intrinsic part of our life, and a way of perceiving reality. There are many application domains where periodic data play a major role. In many of such domains, the huge number of repetitions make the goal of explicitly storing and accessing such data very challenging to the extent of even not being possible, in cases of open ended intervals. In this work, we present a concept to represent periodic data in an implicit way. The representation model we propose captures the notion of periodic granularity provided by the temporal database glossary. We define the algebraic operators, and introduce access algorithms to cope with them and also with temporal range queries, proving that they are correct and complete with respect to the traditional explicit approach. In an experimental evaluation we show the advantages of our approach with respect to traditional explicit approach, in terms of space usage, physical disk I/O's and query response time.Griffith Sciences, School of Information and Communication TechnologyFull Tex
Vertical infrastructure inspection using a quadcopter and shared autonomy control

This paper presents a shared autonomy control scheme for a quadcopter that is suited for inspection of vertical infrastructure — tall man-made structures such as streetlights, electricity poles or the exterior surfaces of buildings. Current approaches to inspection of such structures is slow, expensive, and potentially hazardous.\ud
Low-cost aerial platforms with an ability to hover now have sufficient payload and endurance for this kind of task, but require significant human skill to fly.\ud
We develop a control architecture that enables synergy between the ground-based operator and the aerial inspection robot. An unskilled operator is assisted by onboard\ud
sensing and partial autonomy to safely fly the robot in close proximity to the structure. The operator uses their domain knowledge and problem solving skills to guide the robot in difficult to reach locations to inspect and assess the condition of the infrastructure. The operator commands the robot in a local task coordinate frame with limited degrees of freedom (DOF). For instance: up/down, left/right, toward/away with respect to the infrastructure. We therefore avoid problems of global\ud
mapping and navigation while providing an intuitive interface to the operator.\ud
We describe algorithms for pole detection, robot velocity estimation with respect to the pole, and position estimation in 3D space as well as the control algorithms\ud
and overall system architecture. We present initial results of shared autonomy of a quadrotor with respect to a vertical pole and robot performance is evaluated by\ud
comparing with motion capture data