pinecone/core-2020-05-10-deduplication huggingface dataset:
Efficient incorporation of motionless foreground objects for adaptive background segmentation

Conference on Articulated Motion and Deformable Objects (AMDO), 2006, Andratx (Spain)In this paper, we want to exploit the knowledge obtained from those detected objects which are incorporated into the background model since they cease their movement. These motionless foreground objects should be handled in security domains such as video surveillance. This paper uses an adaptive background modelling algorithm for moving-object detection. Those detected objects which present no motion are identified and added into the background model, so that they will be part of the new background. Such motionless agents are included for further appearance analysis and agent categorization.This work was supported by the project 'Integration of robust perception, learning, and navigation systems in mobile robotics' (J-0929).Peer Reviewe
Anti-parallel coiled coils structure prediction by support vector machine classification

Transactions On Computational Systems Biology V, 4070, pp. 1-8. http://dx.doi.org/10.1007/11790105_1Coiled coils is an important 3-D protein structure with two or more
stranded alpha-helical motif wounded around to form a “knobs-into-holes” structure.
In this paper we propose an SVM classification approach to predict the antiparallel
coiled coils structure based on the primary amino acid sequence. The
training dataset for the machine learning are collected from SOCKET database
which is a SOCKET algorithm predicted coiled coils database. Total 41 sequences
of at least two heptad repeats of the anti-parallel coiled coils motif are extracted
from 12 proteins as the positive datasets. Total 37 of non coiled coils sequences
and parallel coiled coils motif are extracted from 5 proteins as negative datasets.
The normalized positional weight matrix on each heptad register a, b, c, d, e, f and
g is from SOCKET database and is used to generate the positional weight on each
entry. We performed SVM classification using the cross-validated datasets as
training and testing groups. Our result shows 73% accuracy on the prediction of
anti-parallel coiled coils based on the cross-validated data. The result suggests a
useful approach of using SVM to classify the anti-parallel coiled coils based on
the primary amino acid sequence
Learning the reasons why groups of consumers prefer some food products

In this paper we propose a method for learning the reasons why groups of consumers prefer some food products instead of others of the same type. We emphasize the role of groups given that, from a practical point of view, they may represent market segments that demand different products. Our method starts representing in a metric space people preferences; there we are able to define similarity functions that allow a clustering algorithm to discover significant groups of consumers with homogeneous tastes. Finally in each cluster, we learn, with a SVM, a function that explains the tastes of the consumers grouped in the cluster. Additionally, a feature selection process highlights the essential properties of food products that have a major influence on their acceptability. To illustrate our method, a real case of consumers of lamb meat was studied. The panel was formed by 773 people of 216 families from 6 European countries. Different tastes between Northern and Southern families were enhance
Effectiveness evaluation of data mining based IDS

Proceeding of: 6th Industrial Conference on Data Mining, ICDM 2006, Leipzig, Germany, July 14-15, 2006.Data mining has been widely applied to the problem of Intrusion Detection in computer networks. However, the misconception of the underlying problem has led to out of context results. This paper shows that factors such as the probability of intrusion and the costs of responding to detected intrusions must be taken into account in order to compare the effectiveness of machine learning algorithms over the intrusion detection domain. Furthermore, we show the advantages of combining different detection techniques. Results regarding the well known 1999 KDD dataset are shown.Publicad
Points of low height on elliptic curves and surfaces I: Elliptic surfaces over P1 with small d

For each of n = 1, 2, 3 we find the minimal height ˆh(P) of a nontorsion point P of an elliptic curve E over C(T) of discriminant degree d = 12n (equivalently, of arithmetic genus n), and exhibit all (E, P) attaining this minimum. The minimal ˆ h(P) was known to equal 1/30 for n = 1 (Oguiso-Shioda) and 11/420 for n = 2 (Nishiyama), but the formulas for the general (E,P) were not known, nor was the fact that these are also the minima for an elliptic curve of discriminant degree 12n over a function field of any genus. For n = 3 both the minimal height (23/840) and the explicit curves are new. These (E, P) also have the property that that mP is an integral point (a point of na¨ıve height zero) for each m = 1, 2, ..., M, where M = 6, 8, 9 for n = 1, 2, 3; this, too, is maximal in each of the three cases.Mathematic
Shimura Curves for Level-3 Subgroups of the (2,3,7) Triangle Group and Some Other Examples

The (2,3,7) triangle group is known to be associated with a quaternion algebra A/K ramified at two of the three real places of K=Q(cos2π/7) and unramified at all other places of K. This triangle group and its congruence subgroups thus give rise to various Shimura curves and maps between them. We study the genus-1 curves  X_0(3), X_1(3) associated with the congruence subgroups Γ_0(3), Γ_1(3). Since the rational prime 3 is inert in K, the covering X_0(3)/X(1) has degree 28, and its Galois closure X(3)/X(1) has geometric Galois group PSL2(F27). Since X(1) is rational, the covering X_0(3)/X(1) amounts to a rational map of degree 28. We compute this rational map explicitly. We find that X_0(3) is an elliptic curve of conductor 147=3·72 over Q, as is the Jacobian J_1(3) of X_1(3); that these curves are related by an isogeny of degree 13; and that the kernel of the 13-isogeny from J_1(3) to X_0(3) consists of K-rational points. We also use the map X_0(3) --> X(1) to locate some complex multiplication (CM) points on X(1). We conclude by describing analogous behavior of a few Shimura curves associated with quaternion algebras over other cyclic cubic fields.Mathematic
Hard Instances of the Constrained Discrete Logarithm Problem

The discrete logarithm problem (DLP) generalizes to the constrained DLP,
where the secret exponent $x$ belongs to a set known to the attacker. The
complexity of generic algorithms for solving the constrained DLP depends on the
choice of the set. Motivated by cryptographic applications, we study sets with
succinct representation for which the constrained DLP is hard. We draw on
earlier results due to Erd\"os et al. and Schnorr, develop geometric tools such
as generalized Menelaus' theorem for proving lower bounds on the complexity of
the constrained DLP, and construct sets with succinct representation with
provable non-trivial lower bounds
MTcast: Robust and Efficient P2P-Based Video Delivery for Heterogeneous Users

OPODIS 2005 : International Conference On Principles Of Distributed Systems , Dec 12-14, 2005 , Pisa, ItalyIn this paper, we propose a new video delivery method called MTcast (Multiple Transcode based video multicast) which achieves efficient simultaneous video delivery to multiple users with different quality requirements by relying on user nodes to transcode and forward video to other user nodes. In MTcast, each user specifies a quality requirement for a video consisting of bitrate, picture size and frame rate based on the user’s environmental resource limitation. All users can receive video with the specified quality (or near this quality) along a single delivery tree. The main characteristics of MTcast are in its scalability, high user satisfaction degree in received video quality, short startup latency and robustness against node failure. Through simulations, we have confirmed that MTcast can achieve much higher user satisfaction degree and robustness against node failure than the layered multicast method
Towards a transformation chain modeling language

The Model Driven Development (MDD) paradigm stimulates the use of models as the main artifacts for software development. These models can be situated at high levels of abstraction, close to the application's business domain. Many consecutive automatic transformations (a transformation chain) can be applied to these models to add the necessary details in order to generate a concrete implementation. This means that a large part of the total development effort is relocated to the development of transformations and hence we should have the necessary tooling support for designing transformation chains. In this paper we propose a metamodel for a transformation chain modeling language that enables implementation independent composition of transformations. We also propose a concrete syntax for this language that is based on UML activity diagrams.status: publishe
Key research challenges for successfully applying MDD within real-time embedded software development

Model-Driven Development (MDD) is a software development paradigm that promotes the use of models at different levels of abstraction and perform transformations between them to derive one or more concrete application implementations. In this paper we analyze the current status of MDD regarding its applicability for the development of Real-Time Embedded Software. We discuss different modeling framework approaches used to specify the various models, and compare OMG/MDA-based approaches (MOF, UML Profiles and executable UML) with a generic MDD-based approach (GME). Finally, we identify the key challenges for future MDD research in order to successfully apply MDD within RTES Development. These challenges are mainly situated in the field of modeling and standardization of abstraction levels, model transformations and code generation, traceability, and integration of existing software within the MDD development processstatus: publishe
Data access and integration in the ISPIDER proteomics grid

Grid computing has great potential for supporting the integration of complex, fast changing biological data repositories to enable distributed data analysis. One scenario where Grid computing has such potential is provided by proteomics resources which are rapidly being developed with the emergence of affordable, reliable methods to study the proteome. The protein identifications arising from these methods derive from multiple repositories which need to be integrated to enable uniform access to them. A number of technologies exist which enable these resources to be accessed in a Grid environment, but the independent development of these resources means that significant data integration challenges, such as heterogeneity and schema evolution, have to be met. This paper presents an architecture which supports the combined use of Grid data access (OGSA-DAI), Grid distributed querying (OGSA-DQP) and data integration (AutoMed) software tools to support distributed data analysis. We discuss the application of this architecture for the integration of several autonomous proteomics data resources
Predicate introduction under stable and well-founded semantics

This paper studies the transformation of "predicate introduction": replacing a complex formula in an existing logic program by a newly defined predicate. From a knowledge representation perspective, such transformations can be used to eliminate redundancy or to simplify a theory. From a more practical point of view, they can also be used to transform a theory into a normal form imposed by certain inference programs or theorems, e.g., through the elimination of universal quantifiers. In this paper, we study when predicate introduction is equivalence preserving under the stable and well-founded semantics. We do this in the algebraic framework of "approximation theory"; this is a fixpoint theory for non-monotone operators that generalizes all main semantics of various non-monotone logics, including Logic Programming, Default Logic and Autoepistemic Logic. We prove an abstract, algebraic equivalence result and then instantiate this abstract theorem to Logic Programming under the stable and well-founded semantics.status: publishe
Towards region-based memory management for Mercury programs (Extended abstract)

LNCS Vol. 4079Region-based memory management is a form of compiletime memory management, well-known from the functional programming world. This paper describes region-based memory management for the Mercury language using some points-to graphs that model the partition of the memory used by a program into separate regions and distribute the values of the program's variables over the regions. First, a region analysis determines the different regions in the program. Second, the liveness of the regions is computed. Finally, a program transformation adds region annotations to the program for region support. Our approach obtains good results for a restricted set of deterministic Mercury programs and is a valid starting point to make region-based memory management work with general Mercury programs.status: publishe
A Declarative Framework for Security: Secure Concurrent Constraint Programming

International audienceDue to technological advances such as the Internet and mobile computing, Security has become a serious challenge involving several disciplines of Computer Science. In recent years, there has been a growing interest in the analysis of security protocols and one promising approach is the development of formalisms that model communicating processes, in particular Process Calculi. The results are so far encouraging although most remains to be done. In this paper we report our observations and preliminary work on CCP as a framework for security protocols
Memory reuse for CHR

Two Constraint Handling Rules compiler optimizations that drastically reduce the memory footprint of CHR programs are introduced. The reduction is the result of reusing suspension terms, the internal CHR constraint representation, and avoiding the overhead of constraint removal followed by insertion. The optimizations are defined formally and their correctness is proved. Both optimizations were implemented in the K.U.Leuven CHR system. Significant memory savings and speedups were measured on classical and well-known benchmarks.status: publishe
Affective Web Service Design

In this paper, we propose that, in order to improve customer satisfaction, we need to incorporate communication modes (e.g., speech act) in the current standards of web services specifications. We show that with the communication modes, we can estimate various affects on service consumers during their interactions with web services. With this information, a web-service management system can automatically prevent and compensate potential negative affects, and even take advantage of positive affect
MaxMatcher: Biological concept extraction using approximate dictionary lookup

Pricai 2006: Trends In Artificial Intelligence, Proceedings, 4099: pp. 1145-1149. http://dx.doi.org/10.1007/11801603_150Dictionary-based biological concept extraction is still the state-ofthe-
art approach to large-scale biomedical literature annotation and indexing.
The exact dictionary lookup is a very simple approach, but always achieves low
extraction recall because a biological term often has many variants while a dictionary
is impossible to collect all of them. We propose a generic extraction
approach, referred to as approximate dictionary lookup, to cope with term
variations and implement it as an extraction system called MaxMatcher. The
basic idea of this approach is to capture the significant words instead of all
words to a particular concept. The new approach dramatically improves the extraction
recall while maintaining the precision. In a comparative study on
GENIA corpus, the recall of the new approach reaches a 57% recall while the
exact dictionary lookup only achieves a 26% recall
A split-step PSO algorithm in predicting construction litigation outcome

Owing to the highly complicated nature and the escalating cost involved in construction claims, it is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. This paper presents the application of a split-step particle swarm optimization (PSO) model for training perceptrons to predict the outcome of construction claims in Hong Kong. The advantages of global search capability of PSO algorithm in the first step and local fast convergence of Levenberg-Marquardt algorithm in the second step are combined together. The results demonstrate that, when compared with the benchmark backward propagation algorithm and the conventional PSO algorithm, it attains a higher accuracy in a much shorter time.Department of Civil and Environmental EngineeringSeries: Lecture notes in computer science2005-2006 > Academic research: refereed > Publication in refereed journalpreprint_postprin
Density-controlled sampling of parametric surfaces using adaptive space-filling curves

Low-discrepancy point distributions exhibit excellent uniformity properties for sampling in applications such as rendering and measurement. We present an algorithm for generating low-discrepancy point distributions on arbitrary parametric surfaces using the idea of converting the 2D sampling problem into a 1D problem by adaptively mapping a space-filling curve onto the surface. The 1D distribution takes into account the parametric mapping by employing a corrective approach similar to histogram equalisation to ensure that it gives a 2D low-discrepancy point distribution on the surface. This also allows for control over the local density of the distribution, e.g. to place points more densely in regions of higher curvature. To allow for parametric distortion, the space-filling curve is generated adaptively to cover the surface evenly. Experiments show that this approach efficiently generates low-discrepancy distributions on arbitrary parametric surfaces and creates nearly as good results as well-known low-discrepancy sampling methods designed for particular surfaces like planes and spheres. However, we also show that machine-precision limitations may require surface reparameterisation in addition to adaptive sampling
A case study on logging visual activities: Chess game

14th Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN 2005; Izmir; Turkey; 16 June 2005 through 17 June 2005Automatically recognizing and analyzing visual activities in complex environments is a challenging and open-ended problem. In this study this task is performed in a chess game scenario where the rules, actions and the environment are well defined. The purpose here is to detect and observe a FIDE (Fédération International des Ėchecs) compatible chess board, generating a log file of the moves made by human players. A series of basic image processing operations have been applied to perform the desired task. The first step of automatically detecting a chess board is followed by locating the positions of the pieces. After the initial setup is established every move made by a player is automatically detected and verified. Intel® Open Source Computer Vision Library (OpenCV) is used in the current software implementation
Retrieving and Reusing Game Plays for Robot Soccer

Best Paper Award\ud
The original publication is available at http://www.springerlink.comThe problem of defining robot behaviors to completely address a large and complex set of situations is very challenging. We present an approach for robot’s action selection in the robot soccer domain using Case-Based Reasoning techniques. A case represents a snapshot of the game at time t and the actions the robot should perform in that situation. We basically focus our work on the retrieval and reuse steps of the system, presenting the similarity functions and a planning process to adapt the current problem to a case. We present first results of the performance of the system under simulation and the analysis of the parameters used in the approach.Partial funding by the Spanish Ministry of Education and Science project DPI2003-\ud
05193-C02-02. Raquel Ros holds a scholarship from the Generalitat de Catalunya\ud
Government\ud
 his research was partially sponsored by BBNT Solutions, LLC under contract no.\ud
FA8760-04-C-0002 with the US Air Force.Peer reviewe
Instruction Re-Selection for Iterative Modulo Scheduling on High Performance Multi-Issue DSPs

An iterative modulo scheduling is very important for compilers targeting high performance multi-issue digital signal processors. This is because these processors are often severely limited by idle state functional units and thus the reduced idle units can have a positively significant impact on their performance. However, complex instructions, which are used in most recent DSPs such as mac, usually increase data dependence complexity, and such complex dependencies that exist in signal processing applications often restrict modulo scheduling freedom and therefore, become a limiting factor of the iterative modulo scheduler.
In this work, we propose a technique that efficiently reselects instructions of an application loop code considering dependence complexity, which directly resolve the dependence constraint. That is specifically featured for accelerating software pipelining performance by minimizing length of intrinsic cyclic dependencies. To take advantage of this feature, few existing compilers support a loop unrolling based dependence relaxing technique, but only use them for some limited cases. This is mainly because the loop unrolling typically occurs an overhead of huge code size increment, and the iterative modulo scheduling with relaxed dependence techniques for general cases is an NP-hard problem that necessitates complex assignments of registers and functional units. Our technique uses a heuristic to efficiently handle this problem in pre-stage of iterative modulo scheduling without loop unrolling
Fixed Linear Crossing Minimization by Reduction to the Maximum Cut Problem

Many real-life scheduling, routing and locating problems can be formulated as combinatorial optimization problems whose goal is to find a linear layout of an input graph in such a way that the number of edge crossings is minimized. In this paper, we study a restricted version of the linear layout problem where the order of vertices on the line is fixed, the so-called fixed linear crossing number problem (FLCNP). We show that this NP-hard problem can be reduced to the well-known maximum cut problem. The latter problem was intensively studied in the literature; practically efficient exact algorithms based on the branch-and-cut technique have been developed. By an experimental evaluation on a variety of graphs, we prove that using this reduction for solving FLCNP compares favorably to earlier branch-and-bound algorithms
Tree automata with equality constraints modulo equational theories

International audienceThis paper presents new classes of tree automata combining automata with equality test and automata modulo equational theories. We believe that this class has a good potential for application in e.g. software verification. These tree automata are obtained by extending the standard Horn clause representations with equational conditions and rewrite systems. We show in particular that a generalized membership problem (extending the emptiness problem) is decidable by proving that the saturation of tree automata presentations with suitable paramodu- lation strategies terminates. Alternatively our results can be viewed as new decidable classes of first-order formula