bluuebunny/crossref_metadata_2025 huggingface dataset:
Cáncer metastásico con primario desconocido. Una revisión
Authors: Rodríguez Laura , Otero Regino William , Grosso Fabio
Published: July 2018

El tumor metastásico no siempre tiene un origen evidente, hasta en un tercio de los casos nunca se encuentra el tumor primario. Este artículo es una guía de los avances más recientes para mejorar el enfoque diagnóstico y el manejo del paciente, con este fatal y frecuente tumor. El objetivo de este articulo, además de ser una guía, es ayudar a evitar errores comunes y graves. Uno de los errores más importantes es no tener en cuenta el papel fundamental de la confirmación histológica, pues esta puede evitar investigaciones innecesarias.En el articulo también se detallan los componentes de la evaluación estándar, la clasificación según su pronóstico y las indicaciones de la evaluación secundaria, que incluye las indicaciones de la endoscopia alta y baja, los marcadores tumorales, la tomografía por emisión de positrones, el papel que ocupa el perfil genético, la epigenética y el ADN viral. Por otro lado, se indica el momento en que se debe detener la investigación. Recientemente, el tratamiento se ha modificado y con ello parece cambiar la historia de estos pacientes y de sus contrapartes con primario conocido
Incisión Radial y Corte Endoscópico en el manejo de estenosis esofágica anastomótica refractaria. Reporte de un caso
Authors: Arango Molano Lázaro Antonio , Ricardo Andrés Mauricio , Chavarro Orjuela Oliver
Published: July 2018

Una causa poco frecuente de estenosis esofágica es la que se presenta posterior a una anastomosis esofagoyeyunal, la cual puede llevar a comprometer la calidad de vida del paciente, su estado nutricional, requiriendo manejo endoscópico inicial con balón de dilatación o con bujías con el riesgo que ellas generan; pero hay un grupo de pacientes quienes, a pesar de la dilatación, no encuentran mejoría, persistiendo con la estenosis y por lo tanto con su sintomatología. En este último tipo de pacientes con estenosis refractarias al manejo con dilatación, se realiza la técnica de incisión radiada y corte endoscópico con electrobisturi de punta, con buenos resultados. Se presenta el caso de un paciente con una estenosis pos operatoria a nivel de la anastomosis esófago-yeyunal a quien se dio manejo inicial con dilatación, siendo refractaria y quien requirió incisión radiada y corte endoscópico con electrobisturi de punta con mejores resultados en su calidad de vida.
DEVELOPING PROCESS GENRE - BASED APPROACH HANDOUT FOR TEACHING WRITING OF EXPOSITION HORTATORY TEXT FOR SENIOR HIGH SCHOOL STUDENTS
Authors: Handayani Handayani , Jufrizal Jufrizal , Hamzah Hamzah
Published: July 2018

This article is a development research that aims to develop teaching-based writing materials in the process of producing a genre in the form of exposition hortatory. In this study also estimated the level of validity, practicality and effectiveness of teaching materials developed. This research method is done in four stages; definition, design, development and limited deployment; on the students of grade XI IPA 2 SMA Adabiah Padang. After the stages of dissemination is limited, measured product practicability and product effectiveness to the value of student writing. Development results show the validity of the product contained in the category can be used with the improvement with a value of 77.4. Practicality of the product is stated at the value of 88.77 and the effectiveness at the value of 80%. It can be concluded that the product can be used as teaching teaching material.
EDUCATIONAL VALUES IN TEXT STORIES RANDAI "MALANGGA SUMPAH" WORKSHOP BUSTAMI GROUP RANDAI BINTANG TAMPALO KENAGARIAN PADANG LAWEH DISTRICT SIJUNJUNG
Authors: Syuriadi Helki , Hasanuddin Hasanuddin , Manaf Ngusman Abdul
Published: July 2018

The pourpuse this study were to describe the values of education in Story script of Randai titled “Malangga Sumpah” written by Lukman Bustami. The research data was collected by qualitative data, The result of this study is four values of education values in randai script ”Malangga Sumpah” in  by Lukman Bustami there are believe and religious, integrity, caring, and honesty
CHARACTER EDUCATIONAL VALUES IN NOVEL RANTAU SATU MUARA WORKS OF AHMAD FUADI: LITERATURE SOCIOLOGY REVIEW
Authors: Febriana Noni , Thahar Harris Effendi , Ermanto Ermanto
Published: July 2018

This research is motivated by problems in the world of education, that the values of character education get less attention because more directed to the mastery of academic or cognitive aspects. While the actual nonakademik aspect becomes the main foundation in character education less attention, so less embedded in learners. Therefore, this study aims to instill the values of character education contained in the novel Rantau Satu Muara by Ahmad Fuadi. This novel is chosen because it is closely related to the values of character education. The type of this research is qualitative with descriptive analysis method. This research also uses the approach of literary sociology. The data collection is done by the researchers themselves. The researcher recorded and analyzed the data during the research to collect the data taken in the novel text then the researchers themselves processed and analyzed the data in detail. The results of this study are the values of character education in the novel Rantau Satu Muara by Ahmad Fuadi which contains the values of character education, as follows. (1) Educational values of religious characters with indicators of sincerity, diligent worship / prayer, prayer, thanksgiving, and put your trust in. (2) The values of hard work character education with indicators of earnest, unyielding, aspiring and creative. (3) The values of education of the love character of the homeland with nationalist indicators and respect for diversity. (4) Educational values of the character communicative / friendly with indicators of cooperation, social care, love out, affection, kindness and respect the achievements of others. (5) The educational values of the characters love to read with the indicators of love science and diligent reading. (6) Values of character education responsibilities with independent indicators.
Pendidikan Multikultural Berbantuan Metode Pictorial Riddle Untuk Meningkatkan Karakter Kreatif dan Bersahabat Siswa Kelas 3 Sekolah Dasar
Authors: Pratiwi Ika Ari , Masfuah Siti , Rondli Wawan Shokib
Published: July 2018

The differences in social, religious, and cultural status make an impact on social attitudes, the cooperative systems, and how to make friends in school. Efforts to harmonize and unify the diversity of students in schools today is integrated with multicultural education. The purpose of this research is to describe the effectiveness of multicultural education implementation with the help of pictorial riddle method to increase the value of creativity, and friendly/communicative character. In this research, the application of multicultural education is carried out in thematic integrated learning of social and natural science in grade 3 semester 2. The quasi experiment design with control group used pre-test and post-test. The result showed that multicultural education, aided with pictorial riddle method increased the value of creative and friendly/communicative character. The creative character in the experimental class increased significantly from the pretest score of 69 to 85 in the posttest, with the gain of 0.53 in the medium criterion. The increase of friendly/communicative value in the pretest experimental class was 70, increased to 80 at posttest, with the gain of 0,31 in medium criterion.
Pengaruh Model Pembelajaran Probing Prompting Terhadap Hasil Belajar Ditinjau Dari Self-Efficacy
Authors: Novena Viola Vesa , Kriswandani Kriswandani
Published: July 2018

This experimental research aims to find out whether or not: 1) the probing-prompting learning model affects learning achievement, 2) the self-efficafy affects learning achievement, and 3) the model affects the interaction between the learning model and student’s self-efficacy on learning outcomes. The population in this study were all third grade students of Christian Elementary School Eben Haezer Salatiga as many as 72 students. The sampling technique used Cluster Random Sampling and obtained by grade IIIA students as experimental class used probing-prompting learning model and class IIIC as control class using conventional learning model, with each consists of 24 students. Both models are used in science learning on natural resource materials. The instuments used were the test of science learning achievement and questionnaire about student self-efficacy. Data analysis included descriptive and inferential analysis consisting of normality test, homogenity test with Levene's, early equilibrium test with independent sample t test, and hypothesis test with Anava. The whole test was performed with SPSS calculation tool at 0.05 significance level. The results of the hypothesis testing concluded: 1) there was no effect of probing prompting learning model on students' learning achievements; 2) there was an effect of self-efficacy to the learning achievements; the result of student learning with high self-efficacy category is better (higher) than the moderate and low self-efficacy, and student’s learning result with moderate self-efficacy category was better than the ones with low self-efficacy; 3) There was no effect on interaction between the learning model and self-efficacy on student science learning outcomes.
Hubungan Kegiatan Workshop Design Thinking Dengan Sikap Peserta Terhadap Kewirausahaan
Authors: Amalia Sherly , Hafiar Hanny , Budiana Heru Riyanto
Published: July 2018

The workshop was organized by West Java’s Industry and Trade Service (Disperindag) in collaboration with Universitas Padjadjaran with purpose to produce new entrepreneurs. This research aims to discover the extent of the correlation between the workshop and the participants attitude toward entrepreneurship. Theory used in this research is ELM Theory by Richard E. Petty and John T. Cacioppo. This study uses quantitative method. Researcher used Random Sampling, in the population of the participants from the Batch 27 and 28, resulting 65 samples. Based on the recapitulation of the result of correlation and discussion, the result of this research showed that there was a significant correlation between the perception of workshop and the participants attitude toward entrepreneurship. The result indicates that the programs of this workshop significantly affected participant’s will to start becoming entrepreneurs.
CRITÉRIOS PARA DIFERENCIAÇÃO E AUTONOMIA DO DIREITO DIANTE DOS DEMAIS SISTEMAS NORMATIVOS E A COERCITIVIDADE
Authors: Adeodato João Maurício
Published: July 2018

O texto enfrenta a questão da distinção entre o direito e os demais tipos de normas éticas, como as religiosas e morais. Parte das raízes aristotélicas do problema e observa os diversos critérios construídos pela teoria geral do direito ao longo do tempo, tais como heteronomia, alteridade, bilateralidade e outros, arguindo a tese de que podem ser todos reduzidos à coercitividade, entendida como ameaça de violência organizada autorizada por sanção, e rebatendo as objeções anticoercitivistas.
A INDEVIDA CRIMINALIZAÇÃO DA AUTOLAVAGEM DE DINHEIRO
Authors: Dal Santo Luiz Phelipe
Published: July 2018

O presente artigo aborda, como questão principal, a criminalização da lavagem de dinheiro cometida pelo mesmo autor do crime antecedente. No primeiro momento, expõe-se, de modo sucinto, a correspondência entre a sociedade pós-moderna e o moderno Direito Penal. Neste cenário, elementos como neoliberalismo, pretensos riscos sociais, pragmatismo, eficientismo, e populismo penal são correlacionados. Destarte, verifica-se que a presença das referidas características em conjunto não raro enseja a formulação de mecanismos penais simbólicos e de caráter utilitarista, conforme a tipificação penal da lavagem de dinheiro. Tal constatação decorre de breve abordagem quanto ao bem jurídico tutelado pelo supracitado tipo penal, percurso necessário para melhor apreciação do tema central deste estudo. Enfim passa-se a examinar a criminalização da autolavagem, cuja possibilidade fora atestada pelo Supremo Tribunal Federal, no curso do julgamento da Ação Penal 470. Em comparação com doutrinas e ordenamentos jurídicos estrangeiros, são abordadas as teses de inexigibilidade de conduta diversa – decorrente do princípio nemo tenetur se detegere –, do mero exaurimento do crime antecedente e do conflito aparente de normas, na perspectiva do pós-fato copunido e do princípio da consunção. Ao final, conclui-se pela impossibilidade de punição a tal conduta, em respeito ao princípio do ne bis in idem.
ASPECTOS SUBJETIVOS E A DOGMÁTICA PENAL NO CRIME DE LAVAGEM DE DINHEIRO
Authors: Fonseca Pedro H. C.
Published: July 2018

Neste artigo verifica-se como objeto problema a análise dos aspectos subjetivos do delito de lavagem de dinheiro e a dogmática penal a partir da consciência do agente. Aprofunda-se a questão da consciência, do erro e sobretudo, a análise do dolo no delito de branqueamento sob uma perspectiva científica. O texto está inserido dentro do aspecto metodológico jurídico-teórico tendo em vista a investigação que se pretende desenvolver estar relacionada com os aspectos gerais e específicos do campo do Direito penal com a análise dogmática. O texto desenvolve com orientação pelo método de abordagem dialética, pelo que se busca adentrar no fenômeno investigado, para explorar as minúcias do tema.
Feature Integration with Adaptive Importance Maps for Visual Tracking
Authors: Li Aishi , Yang Ming , Yang Wanqi
Published: July 2018

Discriminative correlation filters have recently achieved excellent performance for visual object tracking. The key to success is to make full use of dense sampling and specific properties of circulant matrices in the Fourier domain. However, previous studies don't take into consideration the importance and complementary information of different features, simply concatenating them. This paper investigates an effective method of feature integration for correlation filters, which jointly learns filters, as well as importance maps in each frame. These importance maps borrow the advantages of different features, aiming to achieve complementary traits and improve robustness. Moreover, for each feature, an importance map is shared by its all channels to avoid overfitting. In addition, we introduce a regularization term for the importance maps and use the penalty factor to control the significance of features. Based on handcrafted and CNN features, we implement two trackers, which achieve a competitive performance compared with several state-of-the-art trackers.
Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation
Authors: Li Yang , Li Kan , Wang Xinxin
Published: July 2018

In this paper, we propose a deeply-supervised CNN model for action recognition that fully exploits powerful hierarchical features of CNNs. In this model, we build multi-level video representations by applying our proposed aggregation module at different convolutional layers. Moreover, we train this model in a deep supervision manner, which brings improvement in both performance and efficiency. Meanwhile, in order to capture the temporal structure as well as preserve more details about actions, we propose a trainable aggregation module. It models the temporal evolution of each spatial location and projects them into a semantic space using the Vector of Locally Aggregated Descriptors (VLAD) technique. This deeply-supervised CNN model integrating the powerful aggregation module provides a promising solution to recognize actions in videos. We conduct experiments on two action recognition datasets: HMDB51 and UCF101. Results show that our model outperforms the state-of-the-art methods.
H-Net: Neural Network for Cross-domain Image Patch Matching
Authors: Liu Weiquan , Shen Xuelun , Wang Cheng , Zhang Zhihong , Wen Chenglu , Li Jonathan
Published: July 2018

Describing the same scene with different imaging style or rendering image from its 3D model gives us different domain images. Different domain images tend to have a gap and different local appearances, which raise the main challenge on the cross-domain image patch matching. In this paper, we propose to incorporate AutoEncoder into the Siamese network, named as H-Net, of which the structural shape resembles the letter H. The H-Net achieves state-of-the-art performance on the cross-domain image patch matching. Furthermore, we improved H-Net to H-Net++. The H-Net++ extracts invariant feature descriptors in cross-domain image patches and achieves state-of-the-art performance by feature retrieval in Euclidean space. As there is no benchmark dataset including cross-domain images, we made a cross-domain image dataset which consists of camera images, rendering images from UAV 3D model, and images generated by CycleGAN algorithm. Experiments show that the proposed H-Net and H-Net++ outperform the existing algorithms. Our code and cross-domain image dataset are available at https://github.com/Xylon-Sean/H-Net.
Non-decreasing Payment Rules for Combinatorial Auctions
Authors: Bosshard Vitor , Wang Ye , Seuken Sven
Published: July 2018

Combinatorial auctions are used to allocate resources in domains where bidders have complex preferences over bundles of goods. However, the behavior of bidders under different payment rules is not well understood, and there has been limited success in finding Bayes-Nash equilibria of such auctions due to the computational difficulties involved. In this paper, we introduce non-decreasing payment rules. Under such a rule, the payment of a bidder cannot decrease when he increases his bid, which is a natural and desirable property. VCG-nearest, the payment rule most commonly used in practice, violates this property and can thus be manipulated in surprising ways. In contrast, we show that many other payment rules are non-decreasing. We also show that a non-decreasing payment rule imposes a structure on the auction game that enables us to search for an approximate Bayes-Nash equilibrium much more efficiently than in the general case. Finally, we introduce the utility planes BNE algorithm, which exploits this structure and outperforms a state-of-the-art algorithm by multiple orders of magnitude.
Visible Thermal Person Re-Identification via Dual-Constrained Top-Ranking
Authors: Ye Mang , Wang Zheng , Lan Xiangyuan , Yuen Pong C.
Published: July 2018

Cross-modality person re-identification between the thermal and visible domains is extremely important for night-time surveillance applications. Existing works in this filed mainly focus on learning sharable feature representations to handle the cross-modality discrepancies. However, besides the cross-modality discrepancy caused by different camera spectrums, visible thermal person re-identification also suffers from large cross-modality and intra-modality variations caused by different camera views and human poses. In this paper, we propose a dual-path network with a novel bi-directional dual-constrained top-ranking loss to learn discriminative feature representations. It is advantageous in two aspects: 1) end-to-end feature learning directly from the data without extra metric learning steps, 2) it simultaneously handles the cross-modality and intra-modality variations to ensure the discriminability of the learnt representations. Meanwhile, identity loss is further incorporated to model the identity-specific information to handle large intra-class variations. Extensive experiments on two datasets demonstrate the superior performance compared to the state-of-the-arts.
Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks
Authors: Yu Feiwu , Wu Xinxiao , Sun Yuchao , Duan Lixin
Published: July 2018

Existing deep learning methods of video recognition usually require a large number of labeled videos for training. But for a new task, videos are often unlabeled and it is also time-consuming and labor-intensive to annotate them. Instead of human annotation, we try to make use of existing fully labeled images to help recognize those videos. However, due to the problem of domain shifts and heterogeneous feature representations, the performance of classifiers trained on images may be dramatically degraded for video recognition tasks. In this paper, we propose a novel method, called Hierarchical Generative Adversarial Networks (HiGAN), to enhance recognition in videos (i.e., target domain) by transferring knowledge from images (i.e., source domain). The HiGAN model consists of a \emph{low-level} conditional GAN and a \emph{high-level} conditional GAN. By taking advantage of these two-level adversarial learning, our method is capable of learning a domain-invariant feature representation of source images and target videos. Comprehensive experiments on two challenging video recognition datasets (i.e. UCF101 and HMDB51) demonstrate the effectiveness of the proposed method when compared with the existing state-of-the-art domain adaptation methods.
Markov Random Neural Fields for Face Sketch Synthesis
Authors: Zhang Mingjin , Wang Nannan , Gao Xinbo , Li Yunsong
Published: July 2018

Synthesizing face sketches with both common and specific information from photos has been recently attracting considerable attentions in digital entertainment. However, the existing approaches either make the strict similarity assumption on face sketches and photos, leading to lose some identity-specific information, or learn the direct mapping relationship from face photos to sketches by the simple neural network, resulting in the lack of some common information. In this paper, we propose a novel face sketch synthesis based on the Markov random neural fields including two structures. In the first structure, we utilize the neural network to learn the non-linear photo-sketch relationship and obtain the identity-specific information of the test photo, such as glasses, hairpins and hairstyles. In the second structure, we choose the nearest neighbors of the test photo patch and the sketch pixel synthesized in the first structure from the training data which ensure the common information of Miss or Mr Average. Experimental results on the Chinese University of Hong Kong face sketch database illustrate that our proposed framework can preserve the common structure and capture the characteristic features. Compared with the state-of-the-art methods, our method achieves better results in terms of both quantitative and qualitative experimental evaluations.
Solving Patrolling Problems in the Internet Environment
Authors: Brazdil Tomas , Kucera Antonin , Rehak Vojtech
Published: July 2018

We propose an algorithm for constructing efficient patrolling strategies in the Internet environment, where the protected targets are nodes connected to the network and the patrollers are software agents capable of detecting/preventing undesirable activities on the nodes. The algorithm is based on a novel compositional principle designed for a special class of strategies, and it can quickly construct (sub)optimal solutions even if the number of targets reaches hundreds of millions.
On the Satisfiability Threshold of Random Community-Structured SAT
Authors: Barak-Pelleg Dina , Berend Daniel
Published: July 2018

For both historical and practical reasons, the Boolean satisfiability

problem (SAT) has become one of central importance in computer science.

One type of instances arises when the clauses are chosen uniformly

randomly \textendash{} random SAT. Here, a major problem, recently

solved for sufficiently large clause length, is the satisfiability

threshold conjecture. The value of this threshold is known exactly

only for clause length $2$, and there has been a lot of research

concerning its value for arbitrary fixed clause length.

In this paper, we endeavor to study the satisfiability threshold for random industrial SAT. There is as yet no generally accepted model of industrial SAT, and we confine ourselves to one of the more common features of industrial SAT: the set of variables consists of a number of disjoint communities,

and clauses tend to consist of variables from the same community.

Our main result is that the threshold of random community-structured SAT tends

to be smaller than its counterpart for random SAT. Moreover, under

some conditions, this threshold even vanishes.
Boosting MCSes Enumeration
Authors: Grégoire Éric , Izza Yacine , Lagniez Jean-Marie
Published: July 2018

The enumeration of all Maximal Satisfiable Subsets (MSSes) or all Minimal Correction Subsets (MCSes) of an unsatisfiable CNF Boolean formula is a useful and sometimes necessary step for solving a variety of important A.I. issues. Although the number of different MCSes of a CNF Boolean formula is exponential in the worst case, it remains low in many practical situations; this makes the tentative enumeration possibly successful in these latter cases. In the paper, a technique is introduced that boosts the currently most efficient practical approaches to enumerate MCSes. It implements a model rotation paradigm that allows the set of MCSes to be computed in an heuristically efficient way.
Conflict Directed Clause Learning for Maximum Weighted Clique Problem
Authors: Hebrard Emmanuel , Katsirelos George
Published: July 2018

The maximum clique and minimum vertex cover problems are among Karp's 21 NP-complete problems, and have numerous applications: in combinatorial auctions, for computing phylogenetic trees, to predict the structure of proteins, to analyse social networks, and so forth. Currently, the best complete methods are branch & bound algorithms and rely largely on graph colouring to compute a bound. We introduce a new approach based on SAT and on the "Conflict-Driven Clause Learning" (CDCL) algorithm. We propose an efficient implementation of Babel's bound and pruning rule, as well as a novel dominance rule. Moreover, we show how to compute concise explanations for this inference. Our experimental results show that this approach is competitive and often outperforms the state of the art for finding cliques of maximum weight.
Simpler and Faster Algorithm for Checking the Dynamic Consistency of Conditional Simple Temporal Networks
Authors: Hunsberger Luke , Posenato Roberto
Published: July 2018

Recent work on Conditional Simple Temporal Networks (CSTNs) has

focused on checking the dynamic consistency (DC) property assuming

that execution strategies can react instantaneously to observations.

Three alternative semantics---IR-DC, 0-DC, and π-DC---have been presented.

The most practical DC-checking algorithm for CSTNs has only been analyzed

with respect to the IR-DC semantics, while the 0-DC semantics was shown to have a serious flaw

that the π-DC semantics fixed. Whether the IR-DC semantics had

the same flaw and, if so, what the consequences would be for the

DC-checking algorithm remained open questions.

This paper (1) shows that the IR-DC semantics is also flawed;

(2) shows that one of the constraint-propagation rules from

the IR-DC-checking algorithm is not sound with respect to the IR-DC

semantics;

(3) presents a simpler algorithm, called the π-DC-checking algorithm;

(4) proves that it is sound and complete with respect to the π-DC semantics;

and (5) empirically evaluates the new algorithm.
Solving Exist-Random Quantified Stochastic Boolean Satisfiability via Clause Selection
Authors: Lee Nian-Ze , Wang Yen-Shi , Jiang Jie-Hong R.
Published: July 2018

Stochastic Boolean satisfiability (SSAT) is an expressive language to formulate decision problems with randomness. Solving SSAT formulas has the same PSPACE-complete computational complexity as solving quantified Boolean formulas (QBFs). Despite its broad applications and profound theoretical values, SSAT has received relatively little attention compared to QBF. In this paper, we focus on exist-random quantified SSAT formulas, also known as E-MAJSAT, which is a special fragment of SSAT commonly applied in probabilistic conformant planning, posteriori hypothesis, and maximum expected utility. Based on clause selection, a recently proposed QBF technique, we propose an algorithm to solve E-MAJSAT. Moreover, our method can provide an approximate solution to E-MAJSAT with a lower bound when an exact answer is too expensive to compute. Experiments show that the proposed algorithm achieves significant performance gains and memory savings over the state-of-the-art SSAT solvers on a number of benchmark formulas, and provides useful lower bounds for cases where prior methods fail to compute exact answers.
Solving (Weighted) Partial MaxSAT by Dynamic Local Search for SAT
Authors: Lei Zhendong , Cai Shaowei
Published: July 2018

Partial MaxSAT (PMS) generalizes SAT and MaxSAT by introducing hard clauses and soft clauses. PMS and Weighted PMS (WPMS) have many important real world applications. Local search is one popular method for solving (W)PMS. Recent studies on specialized local search for (W)PMS have led to significant improvements. But such specialized algorithms are complicated with the concepts tailored for hard and soft clauses. In this work, we propose a dynamic local search algorithm, which exploits the structure of (W)PMS by a carefully designed clause weighting scheme. Our solver SATLike adopts a local search framework for SAT and does not need any specialized concept for (W)PMS. Experiments on PMS and WPMS benchmarks from the MaxSAT Evaluations (MSE) 2016 and 2017 show that SATLike significantly outperforms state of the art local search solvers. Also, SATLike significantly narrows the gap between the performance of local search solvers and complete solvers on industrial benchmarks, and performs better than the complete solvers on the MSE2017 benchmarks.
Core-Guided Minimal Correction Set and Core Enumeration
Authors: Narodytska Nina , Bjørner Nikolaj , Marinescu Maria-Cristina , Sagiv Mooly
Published: July 2018

A set of constraints is unsatisfiable if there is no solution that satisfies these constraints. To analyse unsatisfiable problems, the user needs to understand where inconsistencies come from and how they can be repaired. Minimal unsatisfiable cores and correction sets are important subsets of constraints that enable such analysis. In this work, we propose a new algorithm for extracting minimal unsatisfiable cores and correction sets simultaneously. Building on top of the relaxation and strengthening framework, we introduce novel techniques for extracting these sets. Our new solver significantly outperforms several state of the art algorithms on common benchmarks when it comes to extracting correction sets and compares favorably on core extraction.
Stratification for Constraint-Based Multi-Objective Combinatorial Optimization
Authors: Terra-Neves Miguel , Lynce Inês , Manquinho Vasco
Published: July 2018

New constraint-based algorithms have been recently proposed to solve Multi-Objective Combinatorial Optimization (MOCO) problems. These new methods are based on Minimal Correction Subsets (MCSs) or P-minimal models and have shown to be successful at solving MOCO instances when the constraint set is hard to satisfy. However, if the constraints are easy to satisfy, constraint-based tools usually do not perform as well as stochastic methods. For solving such instances, algorithms should focus on dealing with the objective functions. This paper proposes the integration of stratification techniques in constraint-based algorithms for MOCO. Moreover, it also shows how to diversify the stratification among the several objective criteria in order to better approximate the Pareto front of MOCO problems. An extensive experimental evaluation on publicly available MOCO instances shows that the new algorithm is competitive with stochastic methods and it is much more effective than existing constraint-based methods.
A Fast Algorithm for Generalized Arc Consistency of the Alldifferent Constraint
Authors: Zhang Xizhe , Li Qian , Zhang Weixiong
Published: July 2018

The alldifferent constraint is an essential ingredient of most Constraints Satisfaction Problems (CSPs). It has been known that the generalized arc consistency (GAC) of alldifferent constraints can be reduced to the maximum matching problem in a value graph. The redundant edges, which do not appear in any maximum matching of the value graph, can and should be removed from the graph. The existing methods attempt to identify these redundant edges by computing the strongly connected components after finding a maximum matching for the graph. Here, we present a novel theorem for identification of the redundant edges. We show that some of the redundant edges can be immediately detected after finding a maximum matching. Based on this theoretical result, we present an efficient algorithm for processing alldifferent constraints. Experimental results on real problems show that our new algorithm significantly outperforms the-state-of-art approaches.
On the Efficiency of Data Collection for Crowdsourced Classification
Authors: Manino Edoardo , Tran-Thanh Long , Jennings Nicholas R.
Published: July 2018

The quality of crowdsourced data is often highly variable. For this reason, it is common to collect redundant data and use statistical methods to aggregate it. Empirical studies show that the policies we use to collect such data have a strong impact on the accuracy of the system. However, there is little theoretical understanding of this phenomenon. In this paper we provide the first theoretical explanation of the accuracy gap between the most popular collection policies: the non-adaptive uniform allocation, and the adaptive uncertainty sampling and information gain maximisation. To do so, we propose a novel representation of the collection process in terms of random walks. Then, we use this tool to derive lower and upper bounds on the accuracy of the policies. With these bounds, we are able to quantify the advantage that the two adaptive policies have over the non-adaptive one for the first time.
Learning Sequential Correlation for User Generated Textual Content Popularity Prediction
Authors: Wang Wen , Zhang Wei , Wang Jun , Yan Junchi , Zha Hongyuan
Published: July 2018

Popularity prediction of user generated textual content is critical for prioritizing information in the web, which alleviates heavy information overload for ordinary readers. Most previous studies model each content instance separately for prediction and thus overlook the sequential correlations between instances of a specific user. In this paper, we go deeper into this problem based on the two observations for each user, i.e., sequential content correlation and sequential popularity correlation. We propose a novel deep sequential model called User Memory-augmented recurrent Attention Network (UMAN). This model encodes the two correlations by updating external user memories which is further leveraged for target text representation learning and popularity prediction. The experimental results on several real-world datasets validate the benefits of considering these correlations and demonstrate UMAN achieves best performance among several strong competitors.
Personality-Aware Personalized Emotion Recognition from Physiological Signals
Authors: Zhao Sicheng , Ding Guiguang , Han Jungong , Gao Yue
Published: July 2018

Emotion recognition methodologies from physiological signals are increasingly becoming personalized, due to the subjective responses of different subjects to physical stimuli. Existing works mainly focused on modelling the involved physiological corpus of each subject, without considering the psychological factors. The latent correlation among different subjects has also been rarely examined. We propose to investigate the influence of personality on emotional behavior in a hypergraph learning framework. Assuming that each vertex is a compound tuple (subject, stimuli), multi-modal hypergraphs can be constructed based on the personality correlation among different subjects and on the physiological correlation among corresponding stimuli. To reveal the different importance of vertices, hyperedges, and modalities, we assign each of them with weights. The emotion relevance learned on the vertex-weighted multi-modal multi-task hypergraphs is employed for emotion recognition. We carry out extensive experiments on the ASCERTAIN dataset and the results demonstrate the superiority of the proposed method.
Compiling Model Representations for Querying Large ABoxes in Expressive DLs
Authors: Bajraktari Labinot , Ortiz Magdalena , Simkus Mantas
Published: July 2018

Answering ontology mediated queries (OMQs) has received much attention in the last decade, but the big gap between practicable algorithms for lightweight ontologies, that are supported by implemented reasoners, and purely theoretical algorithms for expressive ontologies that are not amenable to implementation, has only increased. Towards narrowing the gap, we propose an algorithm to compile a representation of sets of models for ALCHI ontologies, which is sufficient for answering any monotone OMQ. Rather than reasoning for specific ABoxes, or being fully data-independent, we use generic descriptions of families of ABoxes, given by what we call profiles. Our model compilation algorithm runs on TBoxes and sets of profiles, and supports the incremental addition of new profiles. To illustrate the potential of our approach for OMQ answering, we implement a rewriting into an extension of Datalog for OMQs comprising reachability queries, and provide some promising evaluation results.
First-Order Rewritability of Frontier-Guarded Ontology-Mediated Queries
Authors: Barceló Pablo , Berger Gerald , Lutz Carsten , Pieris Andreas
Published: July 2018

We focus on ontology-mediated queries (OMQs) based on (frontier-)guarded existential rules and (unions of) conjunctive queries, and we investigate the problem of FO-rewritability, i.e., whether an OMQ can be rewritten as a first-order query. We adopt two different approaches. The first approach employs standard two-way alternating parity tree automata. Although it does not lead to a tight complexity bound, it provides a transparent solution based on widely known tools. The second approach relies on a sophisticated automata model, known as cost automata. This allows us to show that our problem is 2EXPTIME-complete. In both approaches, we provide semantic characterizations of FO-rewritability that are of independent interest.
Fast Compliance Checking in an OWL2 Fragment
Authors: Bonatti Piero A.
Published: July 2018

We illustrate a formalization of data usage policies in a fragment of OWL2.  It can be used to encode (i) a company's data protection policy, (ii) data subjects' consent to data processing, and (iii) part of the GDPR (the forthcoming European Data Protection Regulation).  Then a company's policy can be checked for compliance with data subjects' consent and with part of the GDPR by means of subsumption queries.  We provide a complete and tractable structural subsumption algorithm for compliance checking and prove the intractability of a natural generalization of the policy language.
Learning Conceptual Space Representations of Interrelated Concepts
Authors: Bouraoui Zied , Schockaert Steven
Published: July 2018

Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations associate each object from a given domain of interest with a point in a high-dimensional Euclidean space, but they do not model the concepts from this domain, and can thus not directly be used for categorization and related cognitive tasks. A natural solution is to represent concepts as Gaussians, learned from the representations of their instances, but this can only be reliably done if sufficiently many instances are given, which is often not the case. In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other. We show that this leads to substantially better predictions in a knowledge base completion task.
The Complexity of Limited Belief Reasoning—The Quantifier-Free Case
Authors: Chen Yijia , Saffidine Abdallah , Schwering Christoph
Published: July 2018

The classical view of epistemic logic is that an agent knows all the logical consequences of their knowledge base. This assumption of logical omniscience is often unrealistic and makes reasoning computationally intractable. One approach to avoid logical omniscience is to limit reasoning to a certain belief level, which intuitively measures the reasoning "depth".This paper investigates the computational complexity of reasoning with belief levels. First we show that while reasoning remains tractable if the level is constant, the complexity jumps to PSPACE-complete -- that is, beyond classical reasoning -- when the belief level is part of the input. Then we further refine the picture using parameterized complexity theory to investigate how the belief level and the number of non-logical symbols affect the complexity.
Game Description Language and Dynamic Epistemic Logic Compared
Authors: Engesser Thorsten , Mattmüller Robert , Nebel Bernhard , Thielscher Michael
Published: July 2018

Several different frameworks have been proposed to model and reason about knowledge in dynamic multi-agent settings, among them the logic-programming-based game description language GDL-III, and dynamic epistemic logic (DEL), based on possible-worlds semantics. GDL-III and DEL have complementary strengths and weaknesses in terms of ease of modeling and simplicity of semantics. In this paper, we formally study the expressiveness of GDL-III vs. DEL. We clarify the commonalities and differences between those languages, demonstrate how to bridge the differences where possible, and identify large fragments of GDL-III and DEL that are equivalent in the sense that they can be used to encode games or planning tasks that admit the same legal action sequences. We prove the latter by providing compilations between those fragments of GDL-III and DEL.
An Efficient Algorithm To Compute Distance Between Lexicographic Preference Trees
Authors: Li Minyi , Kazimipour Borhan
Published: July 2018

Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LP-trees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies.
Multi-agent Epistemic Planning with Common Knowledge
Authors: Liu Qiang , Liu Yongmei
Published: July 2018

In the past decade, multi-agent epistemic planning has received much attention from both dynamic logic and planning communities. Common knowledge is an essential part of multi-agent modal logics, and plays an important role in coordination and interaction of multiple agents. However, existing implementations of multi-agent epistemic planning provide very limited support for common knowledge, basically static propositional common knowledge. Our work aims to extend an existing multi-agent epistemic planning framework based on higher-order belief change with the capability to deal with common knowledge. We propose a novel normal form for multi-agent KD45 logic with common knowledge. We propose satisfiability solving, revision and update algorithms for this normal form. Based on our algorithms, we implemented a multi-agent epistemic planner with common knowledge called MEPC. Our planner successfully generated solutions for several domains that demonstrate the typical usage of common knowledge.
Two Approaches to Ontology Aggregation Based on Axiom Weakening
Authors: Porello Daniele , Troquard Nicolas , Peñaloza Rafael , Confalonieri Roberto , Galliani Pietro , Kutz Oliver
Published: July 2018

Axiom weakening is a novel technique that allows for fine-grained repair of inconsistent ontologies. In a multi-agent setting, integrating ontologies corresponding to multiple agents may lead to inconsistencies. Such inconsistencies can be resolved after the integrated ontology has been built, or their generation can be prevented during ontology generation.

We implement and compare these two approaches.

First, we study how to repair an inconsistent ontology resulting from a voting-based aggregation of views of heterogeneous agents. Second, we prevent the generation of inconsistencies by letting the agents engage in a turn-based rational protocol about the axioms to be added

to the integrated ontology. We instantiate the two approaches using real-world ontologies and compare them by measuring the levels of satisfaction of the agents w.r.t. the ontology obtained by the two procedures.
Reasoning about Betweenness and RCC8 Constraints in Qualitative Conceptual Spaces
Authors: Schockaert Steven , Li Sanjiang
Published: July 2018

Conceptual spaces are a knowledge representation framework in which concepts are represented geometrically, using convex regions. Motivated by the fact that exact conceptual spaces are usually difficult to obtain, we study the problem of spatial reasoning about qualitative abstractions of such representations. In particular, we consider the problem of deciding whether an RCC8 network extended with constraints about betweenness can be realized using bounded and convex regions in a high-dimensional Euclidean space. After showing that this decision problem is PSPACE-hard in general, we introduce an important fragment for which deciding realizability is NP-complete.
Inconsistency Measures for Repair Semantics in OBDA
Authors: Yun Bruno , Vesic Srdjan , Croitoru Madalina , Bisquert Pierre
Published: July 2018

In this paper, we place ourselves in the Ontology Based Data Access (OBDA) setting and investigate reasoning with inconsistent existential rules knowledge bases. We use the notion of inconsistency measures on sets of facts to rank and filter repairs. We propose a generic framework to answer queries by using the best repairs and study productivity and properties of such a framework.
On Concept Forgetting in Description Logics with Qualified Number Restrictions
Authors: Zhao Yizheng , Schmidt Renate
Published: July 2018

This paper presents a practical method for computing solutions of concept forgetting in the description logic ALCOQ(neg,and,or), basic ALC extended with nominals, qualified number restrictions, role negation, role conjunction and role disjunction. The method is based on a non-trivial generalisation of Ackermann's Lemma, and attempts to compute either semantic solutions of concept forgetting or uniform interpolants in ALCOQ(neg,and,or). It is so far the only approach to concept forgetting in description logics with number restrictions plus nominals, as well as in description logics with ABoxes. Results of an evaluation with a prototypical implementation have shown that the method was successful in more than 90% of the test cases from a large corpus of biomedical ontologies. In only 13.2% of these cases the solutions were semantic solutions.
Solving Separable Nonsmooth Problems Using Frank-Wolfe with Uniform Affine Approximations
Authors: Cheung Edward , Li Yuying
Published: July 2018

Frank-Wolfe methods (FW) have gained significant interest in the machine learning community due to their ability to efficiently solve large problems that admit a sparse structure (e.g. sparse vectors and low-rank matrices). However the performance of the existing FW method hinges on the quality of the linear approximation. This typically restricts FW to smooth functions for which the approximation quality, indicated by a global curvature measure, is reasonably good.

In this paper, we propose a modified FW algorithm amenable to nonsmooth functions, subject to a separability assumption, by optimizing for approximation quality over all affine functions, given a neighborhood of interest. We analyze theoretical properties of the proposed algorithm and demonstrate that it overcomes many issues associated with existing methods in the context of nonsmooth low-rank matrix estimation.